Method for efficient grouping of cache requests for datapath scheduling

ABSTRACT

In a ray tracer, a cache for streaming workloads groups ray requests for coherent successive bounding volume hierarchy traversal operations by sending common data down an attached data path to all ray requests in the group at the same time or about the same time. Grouping the requests provides good performance with a smaller number of cache lines.

CROSS-REFERENCE TO RELATED PATENTS AND APPLICATIONS

This application is related to the following commonly-assigned USpatents and patent applications, the entire contents of each of whichare incorporated by reference: U.S. application Ser. No. 14/563,872titled “Short Stack Traversal of Tree Data Structures” filed Dec. 8,2014; U.S. Pat. No. 9,582,607 titled “Block-Based Bounding VolumeHierarchy”; U.S. Pat. No. 9,552,664 titled “Relative Encoding For ABlock-Based Bounding Volume Hierarchy” as; U.S. Pat. No. 9,569,559titled “Beam Tracing” filed Mar. 18, 2015; U.S. Pat. No. 10,025,879titled “Tree Data Structures Based on a Plurality of Local CoordinateSystems”; U.S. application Ser. No. 14/737,343 titled “Block-BasedLossless Compression of Geometric Data” filed Jun. 11, 2015; and thefollowing US applications filed concurrently herewith:

-   -   (Atty. Docket: 6610-0032/18-AU-127) titled “Method for Continued        Bounding Volume Hierarchy Traversal on Intersection without        Shader Intervention”;    -   (Atty. Docket: 6610-0034/18-SC-0141) titled “A Robust, Efficient        Multiprocessor-Coprocessor Interface”;    -   (Atty. Docket: 6610-0035/18-SC-0144) titled “Query-Specific        Behavioral Modification of Tree Traversal”;    -   (Atty. Docket 6610-0036/18-SC-0145) titled “Conservative        Watertight Ray Triangle Intersection”;    -   (Atty. Docket 6610-0037/18-SC-0149) titled “Method for Handling        Out-of-Order Opaque and Alpha Ray/Primitive Intersections”; and    -   (Atty. Docket 6610-0039/18-AU-0170) titled “Method for Forward        Progress and Programmable Timeouts of Tree Traversal Mechanisms        in Hardware”.

FIELD

The present technology relates to computer graphics, and moreparticularly to ray tracers. More particularly, the technology relatesto hardware acceleration of computer graphics processing including butnot limited to ray tracing. Still more particularly, the examplenon-limiting technology herein relates to a hardware-based traversalcoprocessor that efficiently traverses an acceleration data structuree.g., for real time ray tracing. In still more detail, the technologyherein provides an improved hardware-based scheduling cache memory forray tracing bounding volume hierarchy traversal. The technology hereinhas the advantage of scheduling ray requests and associated testingaccording to their same address grouping to reduce the time that dataneeds to be resident in a data RAM so that other data may also streamthrough the cache. This allows for reduced cache memory size. Smallerarea with high performance allows for more area to be better spent ondata path and other performance and/or memory management enhancements.

BACKGROUND & SUMMARY

If you look around the visual scene before you, you will notice thatsome of the most interesting visual effects you see are produced bylight rays interacting with surfaces. This is because light is the onlything we see. We don't see objects—we see the light that is reflected orrefracted by the objects. Most of the objects we can see reflect light(the color of an object is determined by which parts of light the objectreflects and which parts it absorbs). Shiny surfaces such as metallicsurfaces, glossy surfaces, ceramics, the surfaces of liquids and avariety of others (even the corneas of the human eyes) act as mirrorsthat specularly reflect light. For example, a shiny metal surface willreflect light at the same angle as it hit the surface. An object canalso cast shadows by preventing light from reaching other surfaces thatare behind the object relative to a light source. If you look around,you will notice that the number and kinds of reflections and the number,kinds and lengths of shadows depend on many factors including the numberand type of lights in the scene. A single point light such as a singlefaraway light bulb will produce single reflections and hard shadows.Area light sources such as windows or light panels produce differentkinds of reflection highlights and softer shadows. Multiple lights willtypically produce multiple reflections and more complex shadows (forexample, three separated point light sources will produce three shadowswhich may overlap depending on the positions of the lights relative toan object).

If you move your head as you survey the scene, you will notice that thereflections change in position and shape (the shadows do the same). Bychanging your viewpoint, you are changing the various angles of thelight rays your eyes detect. This occurs instantaneously—you move yourhead and the visual scene changes immediately.

The simple act of drinking a cup of tea is a complex visual experience.The various shiny surfaces of the glossy ceramic cup on the table beforeyou reflect each light in the room, and the cup casts a shadow for eachlight. The moving surface of the tea in the cup is itself reflective.You can see small reflected images of the lights on the tea's surface,and even smaller reflections on the part of the tea's surface where theliquid curves up to meet the walls of the cup. The cup walls also castshadows onto the surface of the liquid in the cup. Lifting the cup toyour mouth causes these reflections and shadows to shift and shimmer asyour viewpoint changes and as the surface of the liquid is agitated bymovement.

We take these complexities of reflections and shadows for granted. Ourbrains are adept at decoding the positions, sizes and shapes of shadowsand reflections and using them as visual cues. This is in part how wediscern the position of objects relative to one another, how wedistinguish one object from another and how we learn what objects aremade of Different object surfaces reflect differently. Specular (mirrortype) reflection of hard metal creates images of reflected objects,while diffuse reflection off of rough surfaces is responsible for colorand lights up objects in a softer way. Shadows can be soft and diffuseor hard and distinct depending on the type of lighting, and the lengthsand directions of the shadows will depend on the angle of the light raysrelative to the object and our eyes.

Beginning artists typically don't try to show reflection or shadows.They tend to draw flat scenes that have no shadows and no reflections orhighlights. The same was true with computer graphics of the past.

Real time computer graphics have advanced tremendously over the last 30years. With the development in the 1980's of powerful graphicsprocessing units (GPUs) providing 3D hardware graphics pipelines, itbecame possible to produce 3D graphical displays based on texture-mappedpolygon primitives in real time response to user input. Such real timegraphics processors were built upon a technology called scan conversionrasterization, which is a means of determining visibility from a singlepoint or perspective. Using this approach, three-dimensional objects aremodelled from surfaces constructed of geometric primitives, typicallypolygons such as triangles. The scan conversion process establishes andprojects primitive polygon vertices onto a view plane and fills in thepoints inside the edges of the primitives. See e.g., Foley, Van Dam,Hughes et al, Computer Graphics: Principles and Practice (2d Ed.Addison-Wesley 1995 & 3d Ed. Addison-Wesley 2014).

Hardware has long been used to determine how each polygon surface shouldbe shaded and texture-mapped and to rasterize the shaded, texture-mappedpolygon surfaces for display. Typical three-dimensional scenes are oftenconstructed from millions of polygons. Fast modern GPU hardware canefficiently process many millions of graphics primitives for eachdisplay frame (every 1/30th or 1/60th of a second) in real time responseto user input. The resulting graphical displays have been used in avariety of real time graphical user interfaces including but not limitedto augmented reality, virtual reality, video games and medical imaging.But traditionally, such interactive graphics hardware has not been ableto accurately model and portray reflections and shadows.

Some have built other technologies onto this basic scan conversionrasterization approach to allow real time graphics systems to accomplisha certain amount of realism in rendering shadows and reflections. Forexample, texture mapping has sometimes been used to simulate reflectionsand shadows in a 3D scene. One way this is commonly done is totransform, project and rasterize objects from different perspectives,write the rasterized results into texture maps, and sample the texturemaps to provide reflection mapping, environment mapping and shadowing.While these techniques have proven to be useful and moderatelysuccessful, they do not work well in all situations. For example,so-called “environment mapping” may often require assuming theenvironment is infinitely distant from the object. In addition, anenvironment-mapped object may typically be unable to reflect itself. Seee.g., hTTP://developer.download.nvidia.com/CgTutorial/cg tutorialchapter07.html. These limitations result because conventional computergraphics hardware—while sufficiently fast for excellent polygonrendering—does not perform the light visualization needed for accurateand realistic reflections and shadows. Some have likened raster/textureapproximations of reflections and shadows as the visual equivalent of AMradio.

There is another graphics technology which does perform physicallyrealistic visibility determinations for reflection and shadowing. It iscalled “ray tracing”. Ray tracing was developed at the end of the 1960'sand was improved upon in the 1980's. See e.g., Apple, “Some Techniquesfor Shading Machine Renderings of Solids” (SJCC 1968) pp. 27-45;Whitted, “An Improved Illumination Model for Shaded Display” Pages343-349 Communications of the ACM Volume 23 Issue 6 (June 1980); andKajiya, “The Rendering Equation”, Computer Graphics (SIGGRAPH 1986Proceedings, Vol. 20, pp. 143-150). Since then, ray tracing has beenused in non-real time graphics applications such as design and filmmaking. Anyone who has seen “Finding Dory” (2016) or other Pixaranimated films has seen the result of the ray tracing approach tocomputer graphics —namely realistic shadows and reflections. See e.g.,Hery et al, “Towards Bidirectional Path Tracing at Pixar” (2016).

Ray tracing is a primitive used in a variety of rendering algorithmsincluding for example path tracing and Metropolis light transport. In anexample algorithm, ray tracing simulates the physics of light bymodeling light transport through the scene to compute all global effects(including for example reflections from shiny surfaces) using rayoptics. In such uses of ray tracing, an attempt may be made to traceeach of many hundreds or thousands of light rays as they travel throughthe three-dimensional scene from potentially multiple light sources tothe viewpoint. Often, such rays are traced relative to the eye throughthe scene and tested against a database of all geometry in the scene.The rays can be traced forward from lights to the eye, or backwards fromthe eye to the lights, or they can be traced to see if paths startingfrom the virtual camera and starting at the eye have a clear line ofsight. The testing determines either the nearest intersection (in orderto determine what is visible from the eye) or traces rays from thesurface of an object toward a light source to determine if there isanything intervening that would block the transmission of light to thatpoint in space. Because the rays are similar to the rays of light inreality, they make available a number of realistic effects that are notpossible using the raster based real time 3D graphics technology thathas been implemented over the last thirty years. Because eachilluminating ray from each light source within the scene is evaluated asit passes through each object in the scene, the resulting images canappear as if they were photographed in reality. Accordingly, these raytracing methods have long been used in professional graphicsapplications such as design and film, where they have come to dominateover raster-based rendering.

The main challenge with ray tracing has generally been speed. Raytracing requires the graphics system to compute and analyze, for eachframe, each of many millions of light rays impinging on (and potentiallyreflected by) each surface making up the scene. In the past, thisenormous amount of computation complexity was impossible to perform inreal time.

One reason modern GPU 3D graphics pipelines are so fast at renderingshaded, texture-mapped surfaces is that they use coherence efficiently.In conventional scan conversion, everything is assumed to be viewedthrough a common window in a common image plane and projected down to asingle vantage point. Each triangle or other primitive is sent throughthe graphics pipeline and covers some number of pixels. All relatedcomputations can be shared for all pixels rendered from that triangle.Rectangular tiles of pixels corresponding to coherent lines of sightpassing through the window may thus correspond to groups of threadsrunning in lock-step in the same streaming processor. All the pixelsfalling between the edges of the triangle are assumed to be the samematerial running the same shader and fetching adjacent groups of texelsfrom the same textures. In ray tracing, in contrast, rays may start orend at a common point (a light source, or a virtual camera lens) but asthey propagate through the scene and interact with different materials,they quickly diverge. For example, each ray performs a search to findthe closest object. Some caching and sharing of results can beperformed, but because each ray potentially can hit different objects,the kind of coherence that GPU's have traditionally taken advantage ofin connection with texture mapped, shaded triangles is not present(e.g., a common vantage point, window and image plane are not there forray tracing). This makes ray tracing much more computationallychallenging than other graphics approaches—and therefore much moredifficult to perform on an interactive basis.

Much research has been done on making the process of tracing rays moreefficient and timely. See e.g., Glassner, An Introduction to Ray Tracing(Academic Press Inc., 1989). Because each ray in ray tracing is, by itsnature, evaluated independently from the rest, ray tracing has beencalled “embarrassingly parallel.” See e.g., Akenine-Möller et al., RealTime Rendering at Section 9.8.2, page 412 (Third Ed. CRC Press 2008). Asdiscussed above, ray tracing involves effectively testing each rayagainst all objects and surfaces in the scene. An optimization called“acceleration data structure” and associated processes allows thegraphics system to use a “divide-and-conquer” approach across theacceleration data structure to establish what surfaces the ray hits andwhat surfaces the ray does not hit. Each ray traverses the accelerationdata structure in an individualistic way. This means that dedicatingmore processors to ray tracing gives a nearly linear performanceincrease. With increasing parallelism of graphics processing systems,some began envisioning the possibility that ray tracing could beperformed in real time. For example, work at Saarland University in themid-2000's produced an early special purpose hardware system forinteractive ray tracing that provided some degree of programmability forusing geometry, vertex and lighting shaders. See Woop et al., “RPU: AProgrammable Ray Processing Unit for Real Time Ray Tracing” (ACM 2005).As another example, Advanced Rendering Technology developed“RenderDrive” based on an array of AR250/350 rendering processorsderived from ARM1 and enhanced with custom pipelines for ray/triangleintersection and SIMD vector and texture math but with no fixed-functiontraversal logic. See e.g.,http://www.graphicshardware.org/previous/www_2001/presentations/Hot3D_Daniel_Hall.pdf

Then, in 2010, NVIDIA took advantage of the high degree of parallelismof NVIDIA GPUs and other highly parallel architectures to develop theOptiX™ ray tracing engine. See Parker et al., “OptiX: A General PurposeRay Tracing Engine” (ACM Transactions on Graphics, Vol. 29, No. 4,Article 66, July 2010). In addition to improvements in API's(application programming interfaces), one of the advances provided byOptiX™ was improving the acceleration data structures used for findingan intersection between a ray and the scene geometry. Such accelerationdata structures are usually spatial or object hierarchies used by theray tracing traversal algorithm to efficiently search for primitivesthat potentially intersect a given ray. OptiX™ provides a number ofdifferent acceleration structure types that the application can choosefrom. Each acceleration structure in the node graph can be a differenttype, allowing combinations of high-quality static structures withdynamically updated ones.

The OptiX™ programmable ray tracing pipeline provided significantadvances, but was still generally unable by itself to provide real timeinteractive response to user input on relatively inexpensive computingplatforms for complex 3D scenes. Since then, NVIDIA has been developinghardware acceleration capabilities for ray tracing. See e.g., U.S. Pat.Nos. 9,582,607; 9,569,559; US20160070820; and US20160070767.

Given the great potential of a truly interactive real time ray tracinggraphics processing system for rendering high quality images ofarbitrary complexity in response for example to user input, further workis possible and desirable.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 illustrates an example non-limiting ray tracing graphics system.

FIG. 2A shows an example specular object.

FIG. 2B shows the example object within a bounding volume.

FIG. 2C shows an example volumetric subdividing of the FIG. 2B boundingvolume.

FIGS. 2D, 2E and 2F show example further levels of volumetricsubdivision of the bounding volume to create a bounding volume hierarchy(BVH).

FIG. 2G shows an example portion of the object comprised of primitivesurfaces, in this case triangles.

FIGS. 3A-3C show example simplified ray tracing tests to determinewhether the ray passes through a bounding volume containing geometry andwhether the ray intersects geometry.

FIG. 4 illustrates an example ray tracing flowchart.

FIGS. 5A-5C show example different ray-primitive intersection scenarios.

FIGS. 6A and 6B show an example of how texture mapping can impactray-primitive intersection results.

FIGS. 7A and 7B illustrate ray instance transforms.

FIG. 8A illustrates an example non-limiting bounding volume hierarchy(BVH).

FIG. 8B shows an example acceleration data structure in the form of agraph or tree.

FIG. 9 shows a simplified example non-limiting traversal co-processorcomprising a tree traversal unit (TTU).

FIG. 10 illustrates an example non-limiting ray tracing shading pipelineflowchart.

FIGS. 11A and 11B illustrate more detailed ray tracing pipelines.

FIG. 12 shows example non-limiting data maintained by the traversalcoprocessor L0 cache;

FIG. 13 is a flowchart of example operations performed by the traversalcoprocessor L0 cache;

FIG. 14 illustrates an example flowchart for generating an image.

FIG. 15 illustrates an example parallel processing unit (PPU).

FIG. 16 illustrates an example memory partition unit.

FIG. 17 illustrates an example general processing cluster (GPC) withinthe parallel processing unit of FIG. 15.

FIG. 18 is a conceptual diagram of a graphics processing pipelineimplemented by the GPC of FIG. 17.

FIGS. 19 and 20 illustrate an example streaming multi-processor.

FIG. 21 is a conceptual diagram of a processing system implemented usingPPUs of FIG. 15.

FIG. 22 expands FIG. 21 to show additional interconnected devices.

DETAILED DESCRIPTION OF NON-LIMITING EMBODIMENTS

The technology herein provides hardware capabilities that accelerate raytracing to such an extent that it brings the power of ray tracing togames and other interactive real time computer graphics, initiallyenabling high effect quality in shadows and reflections and ultimatelyglobal illumination. In practice, this means accelerating ray tracing bya factor of up to an order of magnitude or more over what would bepossible in software on the same graphics rendering system.

In more detail, the example non-limiting technology provides dedicatedhardware to accelerate ray tracing. In non-limiting embodiments, ahardware co-processor (herein referred to as a “traversal coprocessor”or in some embodiments a “tree traversal unit” or “TTU”) acceleratescertain processes supporting interactive ray tracing includingray-bounding volume intersection tests, ray-primitive intersection testsand ray “instance” transforms.

In some non-limiting embodiments, the traversal co-processor performsqueries on an acceleration data structure for processes running onpotentially massively-parallel streaming multiprocessors (SMs). Thetraversal co-processor traverses the acceleration data structure todiscover information about how a given ray interacts with an object theacceleration data structure describes or represents. For ray tracing,the traversal coprocessors are callable as opposed to e.g., fixedfunction units that perform an operation once between logical pipelinestages running different types of threads (e.g., vertex threads andpixel threads).

In some non-limiting embodiments, the acceleration data structurecomprises a hierarchy of bounding volumes (bounding volume hierarchy orBVH) that recursively encapsulates smaller and smaller bounding volumesubdivisions. The largest bounding volume may be termed a “root node.”The smallest subdivisions of such hierarchy of bounding volumes (“leafnodes”) contain items. The items could be primitives (e.g., polygonssuch as triangles) that define surfaces of the object. Or, an item couldbe a sphere that contains a whole new level of the world that exists asan item because it has not been added to the BVH (think of the collarcharm on the cat from “Men in Black” which contained an entire miniaturegalaxy inside of it). If the item comprises primitives, the traversalco-processor tests rays against the primitives to determine which objectsurfaces the rays intersect and which object surfaces are visible alongthe ray.

The traversal co-processor performs a test of each ray against a widerange of bounding volumes, and can cull any bounding volumes that don'tintersect with that ray. Starting at a root node that bounds everythingin the scene, the traversal co-processor tests each ray against smaller(potentially overlapping) child bounding volumes which in turn bound thedescendent branches of the BVH. The ray follows the child pointers forthe bounding volumes the ray hits to other nodes until the leaves orterminal nodes (volumes) of the BVH are reached. Once the traversalco-processor traverses the acceleration data structure to reach aterminal or “leaf” node that contains a geometric primitive, it performsan accelerated ray-primitive intersection test that determines whetherthe ray intersects that primitive (and thus the object surface thatprimitive defines). The ray-primitive test can provide additionalinformation about primitives the ray intersects that can be used todetermine the material properties of the surface required for shadingand visualization. Recursive traversal through the acceleration datastructure enables the traversal co-processor to discover all objectprimitives the ray intersects, or the closest (from the perspective ofthe viewpoint) primitive the ray intersects (which in some cases is theonly primitive that is visible from the viewpoint along the ray).

The traversal co-processor also accelerates the transform of each rayfrom world space into object space to obtain finer and finer boundingbox encapsulations of the primitives and reduce the duplication of thoseprimitives across the scene. Objects replicated many times in the sceneat different positions, orientations and scales can be represented inthe scene as instance nodes which associate a bounding box and leaf nodein the world space BVH with a transformation that can be applied to theworld-space ray to transform it into an object coordinate space, and apointer to an object-space BVH. This avoids replicating the object spaceBVH data multiple times in world space, saving memory and associatedmemory accesses. The instance transform increases efficiency bytransforming the ray into object space instead of requiring the geometryor the bounding volume hierarchy to be transformed into world (ray)space and is also compatible with additional, conventional rasterizationprocesses that graphics processing performs to visualize the primitives.

The presently disclosed non-limiting embodiments thus provide atraversal co-processor, a new subunit of one or a group of streamingmultiprocessor SMs of a 3D graphics processing pipeline. In order tounderstand where the traversal co-processor fits in the overall picture,it may be helpful to understand a few fundamentals of the algorithmemployed by most or all modern ray tracers. But it should be pointed outthat the technology herein provides a generic capability to determine,for a thread running in a GPU, what the nearest visible thing is from agiven point along a specified direction, or if anything lies between twopoints. A common use case for such capability will be in processes thatstart tracing rays from points that have already been rasterized ontriangles using conventional scan conversion techniques. The disclosedtechnology can but does not necessarily replace or substitute for scanconversion technology, and may often augment it and be used inconjunction with scan conversion techniques to enhance images withphotorealistic reflections, shadows and other effects.

Ray Tracing Techniques

Generally, ray tracing is a rendering method in which rays are used todetermine the visibility of various elements in the scene. Ray tracingcan be used to determine if anything is visible along a ray (forexample, testing for occluders between a shaded point on a geometricprimitive and a point on a light source) and can also be used toevaluate reflections (which may for example involve performing atraversal to determine the nearest visible surface along a line of sightso that software running on a streaming processor can evaluate amaterial shading function corresponding to what was hit—which in turncan launch one or more additional rays into the scene according to thematerial properties of the object that was intersected) to determine thelight returning along the ray back toward the eye. In classicalWhitted-style ray tracing, rays are shot from the viewpoint through thepixel grid into the scene, but other path traversals are possible.Typically, for each ray, the closest object is found. This intersectionpoint can then be determined to be illuminated or in shadow by shootinga ray from it to each light source in the scene and finding if anyobjects are in between. Opaque objects block the light, whereastransparent objects attenuate it. Other rays can be spawned from anintersection point. For example, if the intersecting surface is shiny orspecular, rays are generated in the reflection direction. The ray mayaccept the color of the first object intersected, which in turn has itsintersection point tested for shadows. This reflection process isrecursively repeated until a recursion limit is reached or the potentialcontribution of subsequent bounces falls below a threshold. Rays canalso be generated in the direction of refraction for transparent solidobjects, and again recursively evaluated. See Akenine-Möller et al.,cited above. Ray tracing technology thus allows a graphics system todevelop physically correct reflections and shadows that are not subjectto the limitations and artifacts of scan conversion techniques.

Traversal Coprocessor

The basic task the traversal coprocessor performs is to test a rayagainst all primitives (commonly triangles in one embodiment) in thescene and report either the closest hit (according to distance measuredalong the ray) or simply the first (not necessarily closest) hitencountered, depending upon use case. The naïve algorithm would be anO(n) brute-force search. By pre-processing the scene geometry andbuilding a suitable acceleration data structure in advance, however, itis possible to reduce the average-case complexity to O(log n). In raytracing, the time for finding the closest (or for shadows, any)intersection for a ray is typically order O(log n) for n objects when anacceleration data structure is used. For example, bounding volumehierarchies (BVHs) of the type commonly used for modern ray tracingacceleration data structures typically have an O(log n) search behavior.

Bounding Volume Hierarchies

The acceleration data structure most commonly used by modern ray tracersis a bounding volume hierarchy (BVH) comprising nested axis-alignedbounding boxes (AABBs). The leaf nodes of the BVH contain the primitives(e.g., triangles) to be tested for intersection. The BVH is most oftenrepresented by a graph or tree structure data representation. In suchinstances, the traversal coprocessor may be called a “tree traversalunit” or “TTU”.

Given a BVH, ray tracing amounts to a tree search where each node in thetree visited by the ray has a bounding volume for each descendent branchor leaf, and the ray only visits the descendent branches or leaves whosecorresponding bound volume it intersects. In this way, only a smallnumber of primitives must be explicitly tested for intersection, namelythose that reside in leaf nodes intersected by the ray. In the examplenon-limiting embodiments, the traversal coprocessor accelerates bothtree traversal (including the ray-volume tests) and ray-primitive tests.As part of traversal, the traversal coprocessor can also handle“instance transforms”—transforming a ray from world-space coordinatesinto the coordinate system of an instanced mesh (object space) e.g., inorder to avoid the computational complexity of transforming theprimitive vertices into world space. It can do so in a MIMD(multiple-instruction, multiple data) fashion, meaning that the rays arehandled independently once inside the traversal coprocessor.

Example Non-Limiting Real Time Interactive Ray Tracing System

FIG. 1 illustrates an example real time ray interactive tracing graphicssystem 100 for generating images using three dimensional (3D) data of ascene or object(s). System 100 includes an input device 110, aprocessor(s) 120, a graphics processing unit(s) (GPU(s)) 130, memory140, and a display(s) 150. The system shown in FIG. 1 can take on anyform factor including but not limited to a personal computer, a smartphone or other smart device, a video game system, a wearable virtual oraugmented reality system, a cloud-based computing system, avehicle-mounted graphics system, a system-on-a-chip (SoC), etc.

The processor 120 may be a multicore central processing unit (CPU)operable to execute an application in real time interactive response toinput device 110, the output of which includes images for display ondisplay 150. Display 150 may be any kind of display such as a stationarydisplay, a head mounted display such as display glasses or goggles,other types of wearable displays, a handheld display, a vehicle mounteddisplay, etc. For example, the processor 120 may execute an applicationbased on inputs received from the input device 110 (e.g., a joystick, aninertial sensor, an ambient light sensor, etc.) and instruct the GPU 130to generate images showing application progress for display on thedisplay 150.

Based on execution of the application on processor 120, the processormay issue instructions for the GPU 130 to generate images using 3D datastored in memory 140. The GPU 130 includes specialized hardware foraccelerating the generation of images in real time. For example, the GPU130 is able to process information for thousands or millions of graphicsprimitives (polygons) in real time due to the GPU's ability to performrepetitive and highly-parallel specialized computing tasks such aspolygon scan conversion much faster than conventional software-drivenCPUs. For example, unlike the processor 120, which may have multiplecores with lots of cache memory that can handle a few software threadsat a time, the GPU 130 may include hundreds or thousands of processingcores or “streaming multiprocessors” (SMs) 132 running in parallel.

In one example embodiment, the GPU 130 includes a plurality ofprogrammable streaming multiprocessors (SMs) 132, and a hardware-basedgraphics pipeline including a graphics primitive engine 134 and a rasterengine 136. These components of the GPU 130 are configured to performreal-time image rendering using a technique called “scan conversionrasterization” to display three-dimensional scenes on a two-dimensionaldisplay 150. In rasterization, geometric building blocks (e.g., points,lines, triangles, quads, meshes, etc.) of a 3D scene are mapped topixels of the display (often via a frame buffer memory).

The GPU 130 converts the geometric building blocks (i.e., polygonprimitives such as triangles) of the 3D model into pixels of the 2Dimage and assigns an initial color value for each pixel. The graphicspipeline may apply shading, transparency, texture and/or color effectsto portions of the image by defining or adjusting the color values ofthe pixels. The final pixel values may be anti-aliased, filtered andprovided to the display 150 for display. Many software and hardwareadvances over the years have improved subjective image quality usingrasterization techniques at frame rates needed for real-time graphics(i.e., 30 to 60 frames per second) at high display resolutions such as4096×2160 pixels or more on one or multiple displays 150.

Traversal Coprocessor Addition to Architecture

To enable the GPU 130 to perform ray tracing in real time in anefficient manner, the GPU is provided with traversal coprocessor 138coupled to one or more SMs 132. The traversal coprocessor 138 includeshardware components configured to perform operations commonly utilizedin ray tracing algorithms A goal of the traversal coprocessor 138 is toaccelerate operations used in ray tracing to such an extent that itbrings the power of ray tracing to real-time graphics application (e.g.,games), enabling high-quality shadows, reflections, and globalillumination. As discussed in more detail below, the result of thetraversal coprocessor 138 may be used together with or as an alternativeto other graphics related operations performed in the GPU 130.

In the example architecture shown, the new hardware component called a“traversal coprocessor” 138 is used to accelerate certain tasksincluding but not limited to ray tracing. Ray tracing refers to castinga ray into a scene and determining whether and where that ray intersectsthe scene's geometry. This basic ray tracing visibility test is thefundamental primitive underlying a variety of rendering algorithms andtechniques in computer graphics. For example, ray tracing can be usedtogether with or as an alternative to rasterization and z-buffering forsampling scene geometry. It can also be used as an alternative to (or incombination with) environment mapping and shadow texturing for producingmore realistic reflection, refraction and shadowing effects than can beachieved via texturing techniques or other raster “hacks”. To overcomelimitations in image quality that can be achieved with rasterization,system 100 can also generate entire images or parts of images using raytracing techniques. Ray tracing may also be used as the basic primitiveto accurately simulate light transport in physically-based renderingalgorithms such as path tracing, photon mapping, Metropolis lighttransport, and other light transport algorithms.

More specifically, SMs 132 and the traversal coprocessor 138 maycooperate to cast rays into a 3D model and determine whether and wherethat ray intersects the model's geometry. Ray tracing directly simulateslight traveling through a virtual environment or scene. The results ofthe ray intersections together with surface texture, viewing direction,and/or lighting conditions are used to determine pixel color values. Raytracing performed by SMs 132 working with traversal coprocessor 138allows for computer-generated images to capture shadows, reflections,and refractions in ways that can be indistinguishable from photographsor video of the real world. Since ray tracing techniques are even morecomputationally intensive than rasterization due in part to the largenumber of rays that need to be traced, the traversal coprocessor 138 iscapable of accelerating in hardware certain of the morecomputationally-intensive aspects of that process.

In the example non-limiting technology herein, traversal coprocessor 138accelerates both ray-box tests and ray-primitive tests. As part oftraversal, it can also handle at least one level of instance transforms,transforming a ray from world-space coordinates into the coordinatesystem of an instanced mesh. In the example non-limiting embodiments,the traversal coprocessor 138 does all of this in MIMD fashion, meaningthat rays are handled independently once inside the traversalcoprocessor.

In the example non-limiting embodiments, the traversal coprocessor 138operates as a servant (coprocessor) to the SMs (streamingmultiprocessors) 132. In other words, the traversal coprocessor 138 inexample non-limiting embodiments does not operate independently, butinstead follows the commands of the SMs 132 to perform certaincomputationally-intensive ray tracing related tasks much moreefficiently than the SMs 132 could perform themselves.

In the examples shown, the traversal coprocessor 138 receives commandsvia SM 132 instructions and writes results back to an SM register file.For many common use cases (e.g., opaque triangles with at most one levelof instancing), the traversal coprocessor 138 can service the raytracing query without further interaction with the SM 132. Morecomplicated queries (e.g., involving alpha-tested triangles, primitivesother than triangles, or multiple levels of instancing) may requiremultiple round trips. In addition to tracing rays, the traversalcoprocessor 138 is capable of performing more general spatial querieswhere an AABB or the extruded volume between two AABBs (which we call a“beam”) takes the place of the ray. Thus, while the traversalcoprocessor 138 is especially adapted to accelerate ray tracing relatedtasks, it can also be used to perform tasks other than ray tracing.

In addition to the traversal coprocessor 138, the example non-limitingtechnology used to support the system 100 of FIG. 1 provides additionalaccelerated ray tracing enhancements to a number of units as well as asubstantial effort devoted to BVH construction. BVH construction neednot be hardware accelerated (although it may be in some non-limitingembodiments) but could instead be implemented using highly-optimizedsoftware routines running on SMs 132 and/or CPU 120 and/or otherdevelopment systems e.g., during development of an application. Thefollowing exposition describes, among other things, software-visiblebehavior of the traversal coprocessor 138, interfaces to surroundingunits (SMs 132 and the memory subsystem), and additional features thatare part of a complete ray-tracing solution such as certain enhancementsto the group of SMs 132 and the memory caching system.

Traversing an Acceleration Data Structure

A good way to accelerate ray tracing is to use an acceleration datastructure. The acceleration data structure represents the 3D model of anobject or a scene in a manner that will help assist in quickly decidingwhich portion of the object a particular ray is likely to intersect andquickly rejecting large portions of the scene the ray will notintersect. A bounding volume hierarchy (BVH) data structure is one typeof acceleration data structure which can help reduce the number ofintersections to test. The BVH data structure represents a scene orobject with a bounding volume and subdivides the bounding volume intosmaller and smaller bounding volumes terminating in leaf nodescontaining geometric primitives. The bounding volumes are hierarchical,meaning that the topmost level encloses the level below it, that levelencloses the next level below it, and so on. In one embodiment, leafnodes can potentially overlap other leaf nodes in the bounding volumehierarchy.

To illustrate how a bounding volume hierarchy works, FIGS. 2A-2G show ateapot recursively subdivided into smaller and smaller hierarchicalbounding volumes. FIG. 2A shows a teapot object, and FIG. 2B shows abounding volume 202 (in this case a box, cube or rectangularparallelepiped) enclosing the whole teapot. The bounding volume 202,which can be efficiently defined by its vertices, provides an indicationof the spatial location of the object and is typically dimensioned to bejust slightly larger than the object.

The first stage in acceleration structure construction acquires thebounding boxes of the referenced geometry. This is achieved by executingfor each geometric primitive in an object a bounding box procedure thatreturns a conservative axis-aligned bounding box for its input primitivesuch as box 202 shown in FIG. 2B. Using these bounding boxes aselementary primitives for the acceleration structures provides thenecessary abstraction to trace rays against arbitrary user-definedgeometry (including several types of geometry within a singlestructure). Because in FIG. 2B the bounding volume 202 is larger thanand completely contains the teapot, a ray that does not intersectbounding volume cannot intersect the teapot, although a ray that doesintersect the bounding volume may or may not intersect the teapot.Because the bounding volume 202 is readily defined by the x,y,zcoordinates of its vertices in 3D space and a ray is defined by itsx,y,z coordinates in 3D space, the ray-bounding volume test to determinewhether a ray intersects the bounding volume 202 is straightforward(although some transform may be used to adjust to different coordinatesystems, as will be explained below).

FIG. 2C, shows the bounding volume 202 subdivided into smaller containedbounding volumes. While the subdivision scheme shown here for purposesof illustration is a so-called 8-ary subdivision or “octree” in whicheach volume is subdivided into eight smaller volumes of uniform size,many other spatial hierarchies and subdivision schemes are known such asa binary tree, a four-ary tree, a k-d tree, a binary space partitioning(BSP) tree, and a bounding volume hierarchy (BVH) tree. See e.g., U.S.Pat. No. 9,582,607.

Each of the subdivided bounding volumes shown in FIG. 2C can be stillfurther subdivided. FIG. 2D shows one of the subdivided volumes 204 ofFIG. 2C being further subdivided to provide additional subdividedencapsulated bounding volumes. As shown in FIG. 2D, some of thesubdivided bounding volumes include portions of the teapot and some donot. Volumes that do not contain a portion of the teapot are not furthersubdivided because the further subdivisions provide no further spatialinformation about the teapot. Already subdivided bounding volumes thatdo include at least one portion of the teapot can be still furtherrecursively subdivided—like the emergence of each of a succession oflittler and littler cats from the hats of Dr. Seuss's' The Cat In TheHat Comes Back (1958). The portions of the space within bounding volume202 that contain geometry are recursively subdivided to permit thetraversal coprocessor 138 to use the volumetric subdivisions toefficiently discover where the geometry is located relative to any givenray. It can be noted that while a spatial or active subdivision of thevolume is possible, many implementations will create the hierarchicalstructure defining volumes and subvolumes ahead of time. In such cases,the builder may often build the hierarchy up from individual trianglesand not down from the whole scene. Building up means you do not need todetermine if some subdivided volume contains anything since bydefinition it contains what is below it in a hierarchy of volumetricsubdivisions.

FIG. 2E shows a further such subdivision of bounding volume 204 into afurther smaller contained bounding volume 206 containing in this examplejust the spout of the teapot plus another surface on the wall of theteapot, and FIG. 2F shows an additional subdivision of bounding volume206 into still smaller contained subdivision 208 encapsulating the endof the teapot's spout. Depending on the way the BVH is constructed,bounding volume 208 can be further and further subdivided as desired—andtraversal coprocessor 138 enables the FIG. 1 system 100 to efficientlytraverse the BVH down to any arbitrary subdivision level. The number andconfigurations of recursive subdivisions will depend on the complexityand configuration of the 3D object being modeled as well as otherfactors such as desired resolution, distance of the object from theviewpoint, etc.

At some level of subdivision (which can be different levels fordifferent parts of the BVH), the traversal coprocessor 138 encountersgeometry making up the encapsulated object being modeled. Using theanalogy of a tree, the successive volumetric subdivisions are the trunk,branches, boughs and twigs, and the geometric is finally revealed at thevery tips of the tree, namely the leaves. In this case, FIG. 2G showsthe surface of the teapot's spout defined by an example mesh ofgeometric primitives. The geometric primitives shown are triangles butother geometric primitives, such as quads, lines, rectangles, quadrics,patches, or other geometric primitives known to those familiar with thestate of the art, may be used (in one embodiment, such other types ofprimitives may be expressed as or converted into triangles). Thegeometric primitives in the mesh represent the shape of the 3D surfaceof the object being modeled. The example shown here is a mesh, butbounded geometry can include discontinuous geometry such as particlesthat may not be connected. In the example non-limiting embodiments, thetraversal coprocessor 138 also accelerates ray intersection tests withthis geometry to quickly determine which triangles are hit by any givenray. Determining ray-primitive intersections involves comparing thespatial xyz coordinates of the vertices of each primitive with the xyzcoordinates of the ray to determine whether the ray and the surface theprimitive defines occupy the same space. The ray-primitive intersectiontest can be computationally intensive because there may be manytriangles to test. For example, in the mesh shown in FIG. 2G, the spoutof the teapot alone is made up of over a hundred triangles—although itmay be more efficient in some implementations to further volumetricallysubdivide and thereby limit the number of triangles in any such “leafnode” to something like 16 or fewer.

As discussed above, ray tracing procedures determine what geometricprimitives of a scene are intersected by a ray. However, due to thelarge number of primitives in a 3D scene, it may not be efficient orfeasible to test every geometric primitive for an intersection.Acceleration data structures, such as BVH, allow for quick determinationas to which bounding volumes can be ignored, which bounding volumes maycontain intersected geometric primitives, and which intersectedgeometric primitives matter for visualization and which do not.

Ray Intersection Testing

FIGS. 3A-3C illustrate ray tracing applied to the FIG. 2G boundingvolume 208 including triangle mesh 320. FIG. 3A shows a ray 302 in avirtual space including bounding volumes 310 and 315. To determinewhether the ray 302 intersects one or more triangles in the mesh 320,each triangle could be directly tested against the ray 302. But toaccelerate the process (since the object could contain many thousands oftriangles), the ray 302 is first tested against the bounding volumes 310and 315. If the ray 302 does not intersect a bounding volume, then itdoes not intersect any triangles inside of the bounding volume and alltriangles inside the bounding volume can be ignored for purposes of thatray. Because in FIG. 3A the ray 302 misses bounding volume 310, thetriangles of mesh 320 within that bounding volume need not be tested forintersection. While bounding volume 315 is intersected by the ray 302,bounding volume 315 does not contain any geometry and so no furthertesting is required.

On the other hand, if a ray such as ray 304 shown in FIG. 3B intersectsa bounding volume 310 that contains geometry, then the ray may or maynot intersect the geometry inside of the bounding volume so furthertests need to be performed on the geometry itself to find possibleintersections. Because the rays 304, 306 in FIGS. 3B and 3C intersect abounding volume 310 that contains geometry, further tests need to beperformed to determine whether any (and which) of the primitives insideof the bounding volume are intersected. In FIG. 3B, further testing ofthe intersections with the primitives would indicate that even thoughthe ray 304 passes through the bounding volume 310, it does notintersect any of the primitives the bounding volume encloses(alternatively, as mentioned above, bounding volume 310 could be furthervolumetrically subdivided so that a bounding volume intersection testcould be used to reveal that the ray does not intersect any geometry ormore specifically which primitives the ray may intersect).

FIG. 3C shows a situation in which the bounding volume 310 intersectedby ray 306 and contains geometry that ray 306 intersects. Traversalcoprocessor 138 tests the intersections between the ray 306 and theindividual primitives to determine which primitives the ray intersects.

Ray Tracing Operations

FIG. 4 is a flowchart summarizing example ray tracing operations thetraversal coprocessor 138 performs as described above in cooperationwith SM(s) 132. The FIG. 4 operations are performed by traversalcoprocessor 138 in cooperation with its interaction with an SM 132. Thetraversal coprocessor 138 may thus receive the identification of a rayfrom the SM 132 and traversal state enumerating one or more nodes in oneor more BVH's that the ray must traverse. The traversal coprocessor 138determines which bounding volumes of a BVH data structure the rayintersects (the “ray-complet” test 512) and subsequently whether the rayintersects one or more primitives in the intersected bounding volumesand which triangles are intersected (the “ray-primitive test” 520). Inexample non-limiting embodiments, “complets” (compressed treelets)specify root or interior nodes (i.e., volumes) of the bounding volumehierarchy with children that are other complets or leaf nodes of asingle type per complet.

First, the traversal coprocessor 138 inspects the traversal state of theray. If a stack the traversal coprocessor 138 maintains for the ray isempty, then traversal is complete. If there is an entry on the top ofthe stack, the traversal co-processor 138 issues a request to the memorysubsystem to retrieve that node. The traversal co-processor 138 thenperforms a bounding box test 512 to determine if a bounding volume of aBVH data structure is intersected by a particular ray the SM 132specifies (step 512, 514). If the bounding box test determines that thebounding volume is not intersected by the ray (“No” in step 514), thenthere is no need to perform any further testing for visualization andthe traversal coprocessor 138 can return this result to the requestingSM 132. This is because if a ray misses a bounding volume (as in FIG. 3Awith respect to bounding volume 310), then the ray will miss all othersmaller bounding volumes inside the bounding volume being tested and anyprimitives that bounding volume contains.

If the bounding box test performed by the traversal coprocessor 138reveals that the bounding volume is intersected by the ray (“Yes” inStep 514), then the traversal coprocessor determines if the boundingvolume can be subdivided into smaller bounding volumes (step 518). Inone example embodiment, the traversal coprocessor 138 isn't necessarilyperforming any subdivision itself. Rather, each node in the BVH has oneor more children (where each child is a leaf or a branch in the BVH).For each child, there is a bounding volume and a pointer that leads to abranch or a leaf node. When a ray processes a node using traversalcoprocessor 138, it is testing itself against the bounding volumes ofthe node's children. The ray only pushes stack entries onto its stackfor those branches or leaves whose representative bounding volumes werehit. When a ray fetches a node in the example embodiment, it doesn'ttest against the bounding volume of the node—it tests against thebounding volumes of the node's children. The traversal coprocessor 138pushes nodes whose bounding volumes are hit by a ray onto the ray'straversal stack in an order determined by ray configuration. Forexample, it is possible to push nodes onto the traversal stack in theorder the nodes appear in memory, or in the order that they appear alongthe length of the ray, or in some other order. If there are furthersubdivisions of the bounding volume (“Yes” in step 518), then thosefurther subdivisions of the bounding volume are accessed and thebounding box test is performed for each of the resulting subdividedbounding volumes to determine which subdivided bounding volumes areintersected by the ray and which are not. In this recursive process,some of the bounding volumes may be eliminated by test 514 while otherbounding volumes may result in still further and further subdivisionsbeing tested for intersection by traversal coprocessor 138 recursivelyapplying steps 512-518.

Once the traversal coprocessor 138 determines that the bounding volumesintersected by the ray are leaf nodes (“No” in step 518), the traversalcoprocessor performs a primitive (e.g., triangle) intersection test 520to determine whether the ray intersects primitives in the intersectedbounding volumes and which primitives the ray intersects. The traversalcoprocessor 138 thus performs a depth-first traversal of intersecteddescendent branch nodes until leaf nodes are reached. The traversalcoprocessor 138 processes the leaf nodes. If the leaf nodes areprimitive ranges, the traversal coprocessor 138 tests them against theray. If the leaf nodes are instance nodes, the traversal coprocessor 138applies the instance transform. If the leaf nodes are item ranges, thetraversal coprocessor 138 returns them to the requesting SM 132. In theexample non-limiting embodiments, the SM 132 can command the traversalcoprocessor 138 to perform different kinds of ray-primitive intersectiontests and report different results depending on the operations comingfrom an application (or an software stack the application is running on)and relayed by the SM to the TTU. For example, the SM 132 can commandthe traversal coprocessor 138 to report the nearest visible primitiverevealed by the intersection test, or to report all primitives the rayintersects irrespective of whether they are the nearest visibleprimitive. The SM 132 can use these different results for differentkinds of visualization. Once the traversal coprocessor 138 is doneprocessing the leaf nodes, there may be other branch nodes (pushedearlier onto the ray's stack) to test.

Multiple Intersections

In more detail, as shown in FIG. 3C, any given ray may intersectmultiple primitives within a bounding volume. Whether the rayintersection within a given primitive matters for visualization dependson the properties and position of that primitive as well as thevisualization procedures the SM 132 is performing. For example,primitives can be opaque, transparent or partially transparent (i.e.,translucent). Opaque primitives will block a ray from passing throughthe primitive because the eye cannot see through the primitive's opaquesurface. Transparent primitives will allow the ray to pass through(because the eye can see through the transparent primitive) but thesituation may be more complex. For example, transparent primitives mayhave specular properties that cause some portion of the ray to reflect(think of reflection from a window pane) and the rest of the ray to passthrough. Other transparent primitives are used to provide a surface ontowhich a texture is mapped. For example, each individual leaf of a treemay be modeled by a transparent primitive onto which an image of theleaf is texture mapped.

FIGS. 5A-5C illustrate some of these scenarios using an example of threetriangles assumed to be in the same bounding volume and each intersectedby a ray. FIG. 5A illustrates a ray directed towards these threetriangles, with the first triangle the ray encounters relative to theviewpoint being opaque. Because the “front” (from the standpoint of thedirection of the ray from the eye) intersected triangle is opaque, thattriangle will block the ray so the ray will not reach the othertriangles even through it spatially intersects them. In this example,the triangles “behind” the opaque triangle from the viewpoint can beignored (culled) after the intersection of the opaque triangle isidentified because the “front”, opaque triangle hides the othertriangles from the user's view along the ray. Culling is indicated bydotted lines in FIGS. 5A-5C. In this case, the traversal coprocessor 138may only need to report the identification of the first, opaque triangleto the SM 132.

FIG. 5B illustrates a ray directed towards the same three triangles butnow the nearest visible triangle is partially transparent rather thanopaque. Because the nearest visible intersected triangle is at leastpartially transparent, the ray may pass through it to hit the opaquetriangle behind it. In this case, the opaque triangle will be visiblethrough the partially transparent triangle but will block the user'sview of the third triangle along the ray. Here, the traversalcoprocessor 138 may report the identification of both front triangles tothe SM 132 but not report the third, culled triangle even though the rayspatially intersects that third triangle. Order of discovery may matterhere. In the case of an alpha and opaque triangle, if the opaque wasfound first, the traversal coprocessor 138 returns the opaque triangleto the SM 132 with traversal state that will resume testing at the alphatriangle. While there is an implication here that the alpha meanstransparent, it really means “return me to the SM 132 and let the SMdetermine how to handle it.” For example, an alpha triangle might betrimmed according to a texture or function so that portions of thetriangle are cut away (i.e., absent, not transparent). The traversalcoprocessor 138 does not know how the SM 132 will handle the alphatriangles (i.e., it does not handle transparent triangles differentlyfrom trimmed triangles). Thus, alpha triangles may or may not block ortint the light arriving from points beyond them along the ray, and inexample embodiments, they require SM 132 intervention tohandle/determine those things.

FIG. 5C illustrates a scenario in which the first two triangles the rayencounters are partially transparent. Because the first and secondintersected triangles are at least partially transparent, the ray willpass through the first and second triangles to impinge upon thealso-intersecting third opaque triangle. Because third intersectedtriangle is opaque, it will block the ray, and the ray will not impingeupon any other triangles behind the third triangle even though they maybe spatially intersected by it. In this case, the traversal coprocessor138 may report all three triangles to the SM 132 but need not report anyfurther triangles behind the opaque triangle because the opaque triangleblocks the ray from reaching those additional triangles.

In some modes, however, the SM 132 may need to know the identities ofall triangles the ray intersects irrespective of whether they are opaqueor transparent. In those modes, the traversal coprocessor 138 can simplyperform the intersection test and return the identities of all trianglesthe ray spatially intersects (in such modes, the traversal coprocessorwill return the same intersection results for all three scenarios shownin FIGS. 5A-5C) and allow the SM 132 to sort it out—or in some casescommand the traversal coprocessor 138 to do more tests on these sametriangles.

As will be discussed in more detail below, when a ray intersects anopaque triangle, the traversal coprocessor 138 can in certain operationsbe programmed to reduce the length of the ray being tested to thelocation of the opaque triangle intersection so it will not report anytriangles “behind” the intersected triangle. When a partiallytransparent triangle is determined to be intersected by a ray, thetraversal coprocessor 138 will return a more complete list of trianglesthe ray impinges upon for purposes of visualization, and the requestingSM 132 may perform further processing to determine whether, based forexample any texture or other properties of the triangle, the ray will beblocked, passed or partially passed and partially reflected. In exampleembodiments, the traversal coprocessor 138 does not have access totexture properties of triangles and so does not attempt to determinevisualization with respect to those properties.

Textures or Other Surface Modifications

For example, FIGS. 6A and 6B show a transparent triangle 610 with atexture 615 of a leaf applied to the triangle. One could think of atriangle made of Plexiglas with a decal of a leaf applied to it. Asshown in FIG. 6A, the ray 620 intersects the transparent triangle 610 ata point that is outside the applied texture 615. Because the ray 620intersects the triangle outside the applied texture 615, the texturewill not block the ray 620 and the ray will pass through the transparenttriangle 610 without obstruction. This is like being able to see throughthe parts of the Plexiglas triangle that are not covered by the leafdecal. Note that in one example embodiment, the SM 132 makes thevisibility determination since the traversal coprocessor 138 does notnecessarily have access to information concerning the leaf decal. Thetraversal coprocessor 138 helps the SM 132 by returning to the SM theidentification of the triangle that the ray intersects along withinformation concerning the properties of that triangle.

In FIG. 6B, the ray 630 intersects the transparent triangle where thetexture 615 is applied. SM 132 will determine whether subsequenttraversal by the traversal coprocessor 138 is necessary or not based onwhether the texture 615 will block the ray 630 or allow the ray 630 topass through. If the ray 630 is blocked by the texture 615, othertriangles behind the transparent triangle 610, which may have otherwisebeen intersected by the ray 630, will be obstructed by the texture andnot contribute to visibility along the ray. In the example non-limitingembodiments herein, the traversal coprocessor 138 does not have accessto texture information and so it does not attempt to accelerate thisdetermination. Traversal coprocessor 138 may for example return to therequesting SM 132 all intersections between the ray and the varioustriangles within the object, and the SM may then use the graphicsprimitive engine 134 to make further ray tracing visualizationdeterminations. In other example embodiments, traversal coprocessor 138could accelerate some or all of these tests by interacting with thetexture mapping unit and other portions of the 3D graphics pipelinewithin graphics primitive engine 134 to make the necessary visualizationdeterminations.

Coordinate Transforms

FIGS. 2A-3C involve only a single object, namely a teapot. Just as theroom you are in right now contains multiple objects, most 3D scenescontain many objects. For example, a 3D scene containing a teapot willlikely also contain a cup, a saucer, a milk pitcher, a spoon, a sugarbowl, etc. all sitting on a table. In 3D graphics, each of these objectsis typically modelled independently. The graphics system 100 then usescommands from the processor 120 to put all the models together indesired positions, orientations and sizes into the common scene forpurposes of visualization (just as you will set and arrange the tablefor serving tea). What this means is that the SM 132 may commandtraversal processor 138 to analyze the same ray with respect to multipleobjects in the scene. However, the fact that each of these objects willbe transformed in position, orientation and size when placed into thecommon scene is taken into account and accelerated by the traversalcoprocessor 138. In non-limiting example embodiments, the transform fromworld-to-object space is stored in the world space BVH along with aworld-space bounding box. The traversal coprocessor 138 accelerates thetransform process by transforming the ray from world (scene) space intoobject space for purposes of performing the tests shown in FIG. 4. Inparticular, since the transformation of the geometry from object spaceinto world (scene) space is computationally intensive, thattransformation is left to the graphics pipeline graphics primitiveengine 134 and/or raster engine 136 to perform as part of rasterization.The traversal coprocessor 138 instead transforms a given ray from worldspace to the coordinate system of each object defined by an accelerationdata structure and performs its tests in object space.

FIGS. 7A and 7B illustrates how the traversal coprocessor 138 transformsthe same ray into three different object spaces. FIG. 7A shows threeobjects on a table: a cup, a teapot and a pitcher. These three objectsand a table comprise a scene, which exists in world space. A ray thatalso is defined in world space emanates from the viewpoint andintersects each of the three objects.

FIG. 7B shows each of the three objects as defined in object spaces.Each of these three objects is defined by a respective model that existsin a respective object space. The traversal coprocessor 138 in examplenon-limiting embodiments transforms the ray into the object space ofeach object before performing the intersection tests for that object.This “instance transform” saves the computational effort of transformingthe geometry of each object and the associated volumetric subdivisionsof the acceleration data structure from object space to world space forpurposes of the traversal coprocessor 138 performing intersection tests.

The requesting SM 132 keeps track of which objects are in front of whichother objects with respect to each individual ray and resolvesvisibility in cases where one object hides another object, casts ashadow on another object, and/or reflects light toward another object.The requesting SM 132 can use the traversal processor 138 to accelerateeach of these tests.

Example Tree BVH Acceleration Data Structure

FIGS. 8A and 8B show a recursively-subdivided bounding volume of a 3Dscene (FIG. 8A) and a corresponding tree data structure (FIG. 8B) thatmay be accessed by the traversal coprocessor 138 and used forhardware-accelerated operations performed by traversal coprocessor. Thedivision of the bounding volumes may be represented in a hierarchicaltree data structure with the large bounding volume shown in FIG. 2Brepresented by a parent node of the tree and the smaller boundingvolumes represented by children nodes of the tree that are contained bythe parent node. The smallest bounding volumes are represented as leafnodes in the tree and identify one or more geometric primitivescontained within these smallest bounding volumes.

The tree data structure may be stored in memory outside of the traversalcoprocessor 138 and retrieved based on queries the SMs 132 issue to thetraversal coprocessor 138. The tree data structure includes a pluralityof nodes arranged in a hierarchy. The root nodes N1 of the treestructure correspond to bounding volume N1 enclosing all of thetriangles O1-O8. The root node N1 may identify the vertices of thebounding volume N1 and children nodes of the root node.

In FIG. 8A, bounding volume N1 is subdivided into bounding volumes N2and N3. Children nodes N2 and N3 of the tree structure of FIG. 8Bcorrespond to and represent the bounding volumes N2 and N3 shown in FIG.8A. The children nodes N2 and N3 in the tree data structure identify thevertices of respective bounding volumes N2 and N3 in space. Each of thebounding volumes N2 and N3 is further subdivided in this particularexample. Bounding volume N2 is subdivided into contained boundingvolumes N4 and N5. Bounding volume N3 is subdivided into containedbounding volumes N6 and N7. Bounding volume N7 include two boundingvolumes N8 and N9. Bounding volume N8 includes the triangles O7 and O8,and bounding volume N9 includes leaf bounding volumes N10 and N11 as itschild bounding volumes. Leaf bounding volume N10 includes a primitiverange (e.g., triangle range) O10 and leaf bounding volume N11 includesan item range O9. Respective children nodes N4, N5, N6, N8, N10 and N11of the FIG. 8B tree structure correspond to and represent the FIG. 8Abounding volumes N4, N5, N6, N8, N10 and N11 in space.

The FIG. 8B tree is only three to six levels deep so that volumes N4,N5, N6, N8, N10 and N11 constitute “leaf nodes”—that is, nodes in thetree that have no child nodes. FIG. 8A shows that each of leaf nodebounding volumes N4, N5, N6, and N8, contains two triangles of thegeometry in the scene. For example, volumetric subdivision N4 containstriangles O1 & O2; volumetric subdivision N5 contains triangles O3 & O4;volumetric subdivision N6 contains trials O5 & O6; and volumetricsubdivision N8 contains triangles O7 & O8. The tree structure shown inFIG. 8B represents these leaf nodes N4, N5, N6, and N7 by associatingthem with the appropriate ones of triangles O1-O8 of the scene geometry.To access this scene geometry, the traversal coprocessor 138 traversesthe tree data structure of FIG. 8B down to the leaf nodes. In general,different parts of the tree can and will have different depths andcontain different numbers of triangles. Leaf nodes associated withvolumetric subdivisions that contain no geometry need not be explicitlyrepresented in the tree data structure (i.e., the tree is “trimmed”).

According to some embodiments, the subtree rooted at N7 may represent aset of bounding volumes or BVH that is defined in a different coordinatespace than the bounding volumes corresponding to nodes N1-N3. Whenbounding volume N7 is in a different coordinate space from its parentbounding volume N3, an instance node N7′ which provides the raytransformation necessary to traverse the subtree rooted at N7, mayconnect the rest of the tree to the subtree rooted at N7. Instance nodeN7′ connects the bounding volume or BVH corresponding to nodes N1-N3,with the bounding volumes or BVH corresponding to nodes N7 etc. bydefining the transformation from the coordinate space of N1-N3 (e.g.,world space) to the coordinate space of N7 etc. (e.g., object space).

The Internal Structure and Operation of Traversal Coprocessor 138

FIG. 9 shows an example simplified block diagram of traversalcoprocessor 138 including hardware configured to perform acceleratedtraversal operations as described above (a still more detailedimplementation of this traversal coprocessor 138 is described below).Because the traversal coprocessor 138 shown in FIG. 9 is adapted totraverse tree-based acceleration data structures such as shown in FIGS.8A, 8B, it may also be called a “tree traversal unit” or “TTU” 700 (the700 reference number is used to refer to the more detailed non-limitingimplementation of traversal coprocessor 138 shown in FIG. 1). Treetraversal operations may include, for example, determining whether a rayintersects bounding volumes and/or primitives of a tree data structure(e.g., a BVH tree), which tests may involve transforming the ray intoobject space.

The TTU 700 includes dedicated hardware to determine whether a rayintersects bounding volumes and dedicated hardware to determine whethera ray intersects primitives of the tree data structure. In someembodiments, the TTU 700 may perform a depth-first traversal of abounding volume hierarchy using a short stack traversal withintersection testing of supported leaf node primitives and mid-traversalreturn of alpha primitives and unsupported leaf node primitives (items).The intersection of primitives will be discussed with reference totriangles, but other geometric primitives may also be used.

In more detail, TTU 700 includes an intersection management block 722, aray management block 730 and a stack management block 740. Each of theseblocks (and all of the other blocks in FIG. 9) may constitute dedicatedhardware implemented by logic gates, registers, hardware-embedded lookuptables or other combinatorial logic, etc.

The ray management block 730 is responsible for managing informationabout and performing operations concerning a ray specified by an SM 132to the ray management block. The stack management block 740 works inconjunction with traversal logic 712 to manage information about andperform operations related to traversal of a BVH acceleration datastructure. Traversal logic 712 is directed by results of a ray-complettest block 710 that tests intersections between the ray indicated by theray management block 730 and volumetric subdivisions represented by theBVH, using instance transforms as needed. The ray-complet test block 710retrieves additional information concerning the BVH from memory 140 viaan L0 complet cache 752 that is part of the TTU 700. The results of theray-complet test block 710 informs the traversal logic 712 as to whetherfurther recursive traversals are needed. The stack management block 740maintains stacks to keep track of state information as the traversallogic 712 traverses from one level of the BVH to another, with the stackmanagement block pushing items onto the stack as the traversal logictraverses deeper into the BVH and popping items from the stack as thetraversal logic traverses upwards in the BVH. The stack management block740 is able to provide state information (e.g., intermediate or finalresults) to the requesting SM 132 at any time the SM requests.

The intersection management block 722 manages information about andperforms operations concerning intersections between rays andprimitives, using instance transforms as needed. The ray-primitive testblock 720 retrieves information concerning geometry from memory 140 onan as-needed basis via an L0 primitive cache 754 that is part of TTU700. The intersection management block 722 is informed by results ofintersection tests the ray-primitive test and transform block 720performs. Thus, the ray-primitive test and transform block 720 providesintersection results to the intersection management block 722, whichreports geometry hits and intersections to the requesting SM 132.

A Stack Management Unit 740 inspects the traversal state to determinewhat type of data needs to be retrieved and which data path (complet orprimitive) will consume it. The intersections for the bounding volumesare determined in the ray-complet test path of the TTU 700 including oneor more ray-complet test blocks 710 and one or more traversal logicblocks 712. A complet specifies root or interior nodes of a boundingvolume. Thus, a complet may define one or more bounding volumes for theray-complet test. The ray-complet test path of the TTU 700 identifieswhich bounding volumes are intersected by the ray. Bounding volumesintersected by the ray need to be further processed to determine if theprimitives associated with the intersected bounding volumes areintersected. The intersections for the primitives are determined in theray-primitive test path including one or more ray-primitive test andtransform blocks 720 and one or more intersection management blocks 722.

The TTU 700 receives queries from one or more SMs 132 to perform treetraversal operations. The query may request whether a ray intersectsbounding volumes and/or primitives in a BVH data structure. The querymay identify a ray (e.g., origin, direction, and length of the ray) anda BVH data structure and traversal state (e.g., short stack) whichincludes one or more entries referencing nodes in one or more BoundingVolume Hierarchies that the ray is to visit. The query may also includeinformation for how the ray is to handle specific types of intersectionsduring traversal. The ray information may be stored in the raymanagement block 730. The stored ray information (e.g., ray length) maybe updated based on the results of the ray-primitive test.

The TTU 700 may request the BVH data structure identified in the queryto be retrieved from memory outside of the TTU 700. Retrieved portionsof the BVH data structure may be cached in the level-zero (L0) cache 750within the TTU 700 so the information is available for othertime-coherent TTU operations, thereby reducing memory 140 accesses.Portions of the BVH data structure needed for the ray-complet test maybe stored in a L0 complet cache 752 and portions of the BVH datastructure needed for the ray-primitive test may be stored in an L0primitive cache 754.

After the complet information needed for a requested traversal step isavailable in the complet cache 752, the ray-complet test block 710determines bounding volumes intersected by the ray. In performing thistest, the ray may be transformed from the coordinate space of thebounding volume hierarchy to a coordinate space defined relative to acomplet. The ray is tested against the bounding boxes associated withthe child nodes of the complet. In the example non-limiting embodiment,the ray is not tested against the complet's own bounding box because (1)the TTU 700 previously tested the ray against a similar bounding boxwhen it tested the parent bounding box child that referenced thiscomplet, and (2) a purpose of the complet bounding box is to define alocal coordinate system within which the child bounding boxes can beexpressed in compressed form. If the ray intersects any of the childbounding boxes, the results are pushed to the traversal logic todetermine the order that the corresponding child pointers will be pushedonto the traversal stack (further testing will likely require thetraversal logic 712 to traverse down to the next level of the BVH).These steps are repeated recursively until intersected leaf nodes of theBVH are encountered

The ray-complet test block 710 may provide ray-complet intersections tothe traversal logic 612. Using the results of the ray-complet test, thetraversal logic 712 creates stack entries to be pushed to the stackmanagement block 740. The stack entries may indicate internal nodes(i.e., a node that includes one or more child nodes) that need to befurther tested for ray intersections by the ray-complet test block 710and/or triangles identified in an intersected leaf node that need to betested for ray intersections by the ray-primitive test and transformblock 720. The ray-complet test block 710 may repeat the traversal oninternal nodes identified in the stack to determine all leaf nodes inthe BVH that the ray intersects. The precise tests the ray-complet testblock 710 performs will in the example non-limiting embodiment bedetermined by mode bits, ray operations (see below) and culling of hits,and the TTU 700 may return intermediate as well as final results to theSM 132.

The intersected leaf nodes identify primitives that may or may not beintersected by the ray. One option is for the TTU 700 to provide e.g., arange of geometry identified in the intersected leaf nodes to the SM 132for further processing. For example, the SM 132 may itself determinewhether the identified primitives are intersected by the ray based onthe information the TTU 700 provides as a result of the TTU traversingthe BVH. To offload this processing from the SM 132 and therebyaccelerate it using the hardware of the TTU 700, the stack managementblock 740 may issue requests for the ray-primitive and transform block720 to perform a ray-primitive test for the primitives withinintersected leaf nodes the TTU's ray-complet test block 710 identified.In some embodiments, the SM 132 may issue a request for theray-primitive test to test a specific range of primitives and transformblock 720 irrespective of how that geometry range was identified.

After making sure the primitive data needed for a requestedray-primitive test is available in the primitive cache 754, theray-primitive and transform block 710 may determine primitives that areintersected by the ray using the ray information stored in the raymanagement block 730. The ray-primitive test block 720 provides theidentification of primitives determined to be intersected by the ray tothe intersection management block 722.

The intersection management block 722 can return the results of theray-primitive test to the SM 132. The results of the ray-primitive testmay include identifiers of intersected primitives, the distance ofintersections from the ray origin and other information concerningproperties of the intersected primitives. In some embodiments, theintersection management block 722 may modify an existing ray-primitivetest (e.g., by modifying the length of the ray) based on previousintersection results from the ray-primitive and transform block 710.

The intersection management block 722 may also keep track of differenttypes of primitives. For example, the different types of trianglesinclude opaque triangles that will block a ray when intersected andalpha triangles that may or may not block the ray when intersected ormay require additional handling by the SM. Whether a ray is blocked ornot by a transparent triangle may for example depend on texture(s)mapped onto the triangle, area of the triangle occupied by the texture(see FIGS. 6A and 6B) and the way the texture modifies the triangle. Forexample, transparency (e.g., stained glass) in some embodiments requiresthe SM 132 to keep track of transparent object hits so they can besorted and shaded in ray-parametric order, and typically don't actuallyblock the ray. Meanwhile, alpha “trimming” allows the shape of theprimitive to be trimmed based on the shape of a texture mapped onto theprimitive—for example, cutting a leaf shape out of a triangle. (Notethat in raster graphics, transparency is often called “alpha blending”and trimming is called “alpha test”). In other embodiments, the TTU 700can push transparent hits to queues in memory for later handling by theSM 132 and directly handle trimmed triangles by sending requests to thetexture unit. Each triangle may include a designator to indicate thetriangle type. The intersection management block 722 is configured tomaintain a result queue for tracking the different types of intersectedtriangles. For example, the result queue may store one or moreintersected opaque triangle identifiers in one queue and one or moretransparent triangle identifiers in another queue.

For opaque triangles, the ray intersection can be fully determined inthe TTU 700 because the area of the opaque triangle blocks the ray fromgoing past the surface of the triangle. For transparent triangles, rayintersections cannot in some embodiments be fully determined in the TTU700 because TTU 700 performs the intersection test based on the geometryof the triangle and may not have access to the texture of the triangleand/or area of the triangle occupied by the texture (in otherembodiments, the TTU may be provided with texture information by thetexture mapping block of the graphics pipeline). To fully determinewhether the triangle is intersected, information about transparenttriangles the ray-primitive and transform block 710 determines areintersected may be sent to the SM 132, for the SM to make the fulldetermination as to whether the triangle affects visibility along theray.

The SM 132 can resolve whether or not the ray intersects a textureassociated with the transparent triangle and/or whether the ray will beblocked by the texture. The SM 132 may in some cases send a modifiedquery to the TTU 700 (e.g., shortening the ray if the ray is blocked bythe texture) based on this determination.

In one embodiment, the TTU 700 may be configured to return all trianglesdetermined to intersect the ray to the SM 132 for further processing.Because returning every triangle intersection to the SM 132 for furtherprocessing is costly in terms of interface and thread synchronization,the TTU 700 may be configured to hide triangles which are intersectedbut are provably capable of being hidden without a functional impact onthe resulting scene. For example, because the TTU 700 is provided withtriangle type information (e.g., whether a triangle is opaque ortransparent), the TTU 700 may use the triangle type information todetermine intersected triangles that are occluded along the ray byanother intersecting opaque triangle and which thus need not be includedin the results because they will not affect the visibility along theray. As discussed above with reference to FIGS. 5A-5C, if the TTU 700knows that a triangle is occluded along the ray by an opaque triangle,the occluded triangle can be hidden from the results without impact onvisualization of the resulting scene.

The intersection management block 722 may include a result queue forstoring hits that associate a triangle ID and information about thepoint where the ray hit the triangle. When a ray is determined tointersect an opaque triangle, the identity of the triangle and thedistance of the intersection from the ray origin can be stored in theresult queue. If the ray is determined to intersect another opaquetriangle, the other intersected opaque triangle can be omitted from theresult if the distance of the intersection from the ray origin isgreater than the distance of the intersected opaque triangle alreadystored in the result queue. If the distance of the intersection from theray origin is less than the distance of the intersected opaque trianglealready stored in the result queue, the other intersected opaquetriangle can replace the opaque triangle stored in the result queue.After all of the triangles of a query have been tested, the opaquetriangle information stored in the result queue and the intersectioninformation may be sent to the SM 132.

In some embodiments, once an opaque triangle intersection is identified,the intersection management block 722 may shorten the ray stored in theray management block 730 so that bounding volumes (which may includetriangles) behind the intersected opaque triangle (along the ray) willnot be identified as intersecting the ray.

The intersection management block 722 may store information aboutintersected transparent triangles in a separate queue. The storedinformation about intersected transparent triangles may be sent to theSM 132 for the SM to resolve whether or not the ray intersects a textureassociated with the triangle and/or whether the texture blocks the ray.The SM may return the results of this determination to the TTU 700and/or modify the query (e.g., shorten the ray if the ray is blocked bythe texture) based on this determination.

Example Ray Tracing Shading Pipeline

FIG. 10 shows an exemplary ray tracing shading pipeline 900 that may beperformed by SM 132 and accelerated by TTU 700. The ray tracing shadingpipeline 900 starts by an SM 132 invoking ray generation 910 and issuinga corresponding ray tracing request to the TTU 700. The ray tracingrequest identifies a single ray cast into the scene and asks the TTU 700to search for intersections with an acceleration data structure the SM132 also specifies. The TTU 700 traverses (FIG. 10 block 920) theacceleration data structure to determine intersections or potentialintersections between the ray and the volumetric subdivisions andassociated triangles the acceleration data structure represents.Potential intersections can be identified by finding bounding volumes inthe acceleration data structure that are intersected by the ray.Descendants of non-intersected bounding volumes need not be examined.

For triangles within intersected bounding volumes, the TTU 700ray-primitive test block 720 performs an intersection 930 process todetermine whether the ray intersects the primitives. The TTU 700 returnsintersection information to the SM 132, which may perform an “any hit”shading operation 940 in response to the intersection determination. Forexample, the SM 132 may perform (or have other hardware perform) atexture lookup for an intersected primitive and decide based on theappropriate texel's value how to shade a pixel visualizing the ray. TheSM 132 keeps track of such results since the TTU 700 may return multipleintersections with different geometry in the scene in arbitrary order.

Alternatively, primitives that the TTU 700 determines are intersectedmay be further processed to determine 950 whether they should be shadedas a miss 960 or as a closest hit 970. The SM 132 can for exampleinstruct the TTU 700 to report a closest hit in the specified geometry,or it may instruct the TTU to report all hits in the specified geometry.For example, it may be up to the SM 132 to implement a “miss” shadingoperation for a primitive the TTU 700 determines is intersected based onimplemented environment lookups (e.g., approximating the appearance of areflective surface by means of a precomputed texture image) such asshown in FIGS. 6A & 6B. The SM 132 may perform a closest hit shadingoperation to determine the closest intersected primitive based onmaterial evaluations and texture lookups in response to closest hitreports the TTU 700 provided for particular object geometry.

The FIG. 11A more detailed diagram of a ray-tracing pipeline flowchartshows the data flow and interaction between components for arepresentative use case: tracing rays against a scene containinggeometric primitives, with instance transformations handled in hardware.In one example non-limiting embodiment, the ray-tracing pipeline of FIG.11A is essentially software-defined (which in example embodiments meansit is determined by the SMs 132) but makes extensive use of hardwareacceleration by TTU 700. Key components include the SM 132 (and the restof the compute pipeline), the TTU 700 (which serves as a coprocessor toSM), and the L1 cache and downstream memory system, from which the TTUfetches BVH and triangle data.

The pipeline shown in FIG. 11A shows that bounding volume hierarchycreation 1002 can be performed ahead of time by a development system. Italso shows that ray creation and distribution 1004 are performed orcontrolled by the SM 132 or other software in the example embodiment, asis shading (which can include lighting and texturing). The examplepipeline includes a “top level” BVH tree traversal 1006, raytransformation 1014, “bottom level” BVH tree traversal 1018, and aray/triangle (or other primitive) intersection 1026 that are eachperformed by the TTU 700. These do not have to be performed in the ordershown, as handshaking between the TTU 700 and the SM 132 determines whatthe TTU 700 does and in what order.

The SM 132 presents one or more rays to the TTU 700 at a time. Each raythe SM 132 presents to the TTU 700 for traversal may include the ray'sgeometric parameters, traversal state, and the ray's ray flags, modeflags and ray operations information. In an example embodiment, a rayoperation (RayOp) provides or comprises an auxiliary arithmetic and/orlogical test to suppress, override, and/or allow storage of anintersection. The traversal stack may also be used by the SM 132 tocommunicate certain state information to the TTU 700 for use in thetraversal. A new ray query may be started with an explicit traversalstack. For some queries, however, a small number of stack initializersmay be provided for beginning the new query of a given type, such as,for example: traversal starting from a complet; intersection of a raywith a range of triangles; intersection of a ray with a range oftriangles, followed by traversal starting from a complet; vertex fetchfrom a triangle buffer for a given triangle, etc. In some embodiments,using stack initializers instead of explicit stack initializationimproves performance because stack initializers require fewer streamingprocessor registers and reduce the number of parameters that need to betransmitted from the streaming processor to the TTU.

In the example embodiment, a set of mode flags the SM 132 presents witheach query (e.g., ray) may at least partly control how the TTU 700 willprocess the query when the query intersects the bounding volume of aspecific type or intersects a primitive of a specific primitive type.The mode flags the SM 132 provides to the TTU 700 enable the ability bythe SM and/or the application to e.g., through a RayOp, specify anauxiliary arithmetic or logical test to suppress, override, or allowstorage of an intersection. The mode flags may for example enabletraversal behavior to be changed in accordance with such aspects as, forexample, a depth (or distance) associated with each bounding volumeand/or primitive, size of a bounding volume or primitive in relation toa distance from the origin or the ray, particular instances of anobject, etc. This capability can be used by applications to dynamicallyand/or selectively enable/disable sets of objects for intersectiontesting versus specific sets or groups of queries, for example, to allowfor different versions of models to be used when application statechanges (for example, when doors open or close) or to provide differentversions of a model which are selected as a function of the length ofthe ray to realize a form of geometric level of detail, or to allowspecific sets of objects from certain classes of rays to make somelayers visible or invisible in specific views.

In addition to the set of mode flags which may be specified separatelyfor the ray-complet intersection and for ray-primitive intersections,the ray data structure may specify other RayOp test related parameters,such as ray flags, ray parameters and a RayOp test. The ray flags can beused by the TTU 700 to control various aspects of traversal behavior,back-face culling, and handling of the various child node types, subjectto a pass/fail status of an optional RayOp test. RayOp tests addflexibility to the capabilities of the TTU 700, at the expense of somecomplexity. The TTU 700 reserves a “ray slot” for each active ray it isprocessing, and may store the ray flags, mode flags and/or the RayOpinformation in the corresponding ray slot buffer within the TTU duringtraversal.

In the example shown in FIG. 11A, the TTU 700 performs a top level treetraversal 1006 and a bottom level tree traversal 1018. In the exampleembodiment, the two level traversal of the BVH enables fast ray tracingresponses to dynamic scene changes.

Ray transformation 1014 provides the appropriate transition from the toplevel tree traversal 1006 to the bottom level tree traversal 1018 bytransforming the ray, which may be used in the top level traversal in afirst coordinate space (e.g., world space), to a different coordinatespace (e.g., object space) of the BVH of the bottom level traversal. Anexample BVH traversal technique using a two level traversal is describedin previous literature, see, e.g., Woop, “A Ray Tracing HardwareArchitecture for Dynamic Scenes”, Universitat des Saarlandes, 2004, butembodiments are not limited thereto.

In some embodiments, the top level traversal (in world space) is made ina BVH that may be dynamically recalculated (e.g., by SM 132) in responseto changes in the scene, and the bottom level traversal is made in a BVHof bounding volumes that remain static or substantially static even whenchanges in the scene occur. The bounding volumes in the BVH used for thebottom level tree traversal 1018 (in object space) may encompass moredetailed information regarding the scene geometry than the respectivebounding volumes used in the top level tree traversal 1006, therebyavoiding or at least reducing the modification of the bottom leveltraversal BVH in response to scene changes. This helps to speed up raytracing of dynamic scenes.

Example Top Level Tree Traversal

The top level tree traversal 1006 by TTU 700 receives complets from theL1 cache 1012, and provides an instance to the ray transformation 1014for transformation or a miss/end output 1013 to the SM 132 for closesthit shader 1015 processing by the SM (this block can also operaterecursively based on non-leaf nodes/no hit conditions). In the top leveltree traversal 1006, a next complet fetch step 1008 fetches the nextcomplet to be tested for ray intersection in step 1010 from the memoryand/or cache hierarchy and ray-bounding volume intersection testing isdone on the bounding volumes in the fetched complet.

As described above, an instance node connects one BVH to another BVHwhich is in a different coordinate system. When a child of theintersected bounding volume is an instance node, the ray transformation1014 is able to retrieve an appropriate transform matrix from the L1cache 1016. The TTU 700, using the appropriate transform matrix,transforms the ray to the coordinate system of the child BVH. U.S.patent application Ser. No. 14/697,480, which is already incorporated byreference, describes transformation nodes that connect a first set ofnodes in a tree to a second set of nodes where the first and second setsof nodes are in different coordinate systems. The instance nodes inexample embodiments may be similar to the transformation nodes in U.S.application Ser. No. 14/697,480. In an alternative, non-instancing modeof TTU 700 shown in FIG. 11B, the TTU does not execute a “bottom” leveltree traversal 1018 and non-instanced tree BVH traversals are performedby blocks 1008, 1010 e.g., using only one stack. The TTU 700 can switchbetween the FIG. 11A instanced operations and the FIG. 11B non-instancedoperations based on what it reads from the BVH and/or query type. Forexample, a specific query type may restrict the TTU to use just thenon-instanced operations. In such a query, any intersected instancenodes would be returned to the SM.

In some non-limiting embodiments, ray-bounding volume intersectiontesting in step 1010 is performed on each bounding volume in the fetchedcomplet before the next complet is fetched. Other embodiments may useother techniques, such as, for example, traversing the top leveltraversal BVH in a depth-first manner. U.S. Pat. No. 9,582,607, alreadyincorporated by reference, describes one or more complet structures andcontents that may be used in example embodiments. U.S. Pat. No.9,582,607 also describes an example traversal of complets.

When a bounding volume is determined to be intersected by the ray, thechild bounding volumes (or references to them) of the intersectedbounding volume are kept track of for subsequent testing forintersection with the ray and for traversal. In example embodiments, oneor more stack data structures is used for keeping track of childbounding volumes to be subsequently tested for intersection with theray. In some example embodiments, a traversal stack of a small size maybe used to keep track of complets to be traversed by operation of thetop level tree traversal 1006, and primitives to be tested forintersection, and a larger local stack data structure can be used tokeep track of the traversal state in the bottom level tree traversal1018.

Example Bottom Level Tree Traversal

In the bottom level tree traversal 1018, a next complet fetch step 1022fetches the next complet to be tested for ray intersection in step 1024from the memory and/or cache hierarchy 1020 and ray-bounding volumeintersection testing is done on the bounding volumes in the fetchedcomplet. The bottom level tree traversal, as noted above, may includecomplets with bounding volumes in a different coordinate system than thebounding volumes traversed in the upper level tree traversal. The bottomlevel tree traversal also receives complets from the L1 cache and canoperate recursively or iteratively within itself based onnon-leaf/no-hit conditions and also with the top level tree traversal1006 based on miss/end detection. Intersections of the ray with thebounding volumes in the lower level BVH may be determined with the raytransformed to the coordinate system of the lower level completretrieved. The leaf bounding volumes found to be intersected by the rayin the lower level tree traversal are then provided to the ray/triangleintersection 1026.

The leaf outputs of the bottom level tree traversal 1018 are provided tothe ray/triangle intersection 1026 (which has L0 cache access as well asability to retrieve triangles via the L1 cache 1028). The L0 complet andtriangle caches may be small read-only caches internal to the TTU 700.The ray/triangle intersection 1026 may also receive leaf outputs fromthe top level tree traversal 1006 when certain leaf nodes are reachedwithout traversing an instanced BVH.

After all the primitives in the primitive range have been processed, theIntersection Management Unit inspects the state of the result Queue andcrafts packets to send to the Stack Management Unit and/or RayManagement Unit to update the ray's attributes and traversal state, setup the ray's next traversal step, and/or return the ray to the SM 132(if necessary). If the result queue contains opaque or alphaintersections found during the processing of the primitive range thenthe Intersection Management Unit signals the parametric length (t) ofthe nearest opaque intersection in the result queue to the raymanagement unit to record as the ray's tmax to shorten the ray. Toupdate the traversal state to set up the ray's next traversal step theIntersection Management Unit signals to the Stack Management Unitwhether an opaque intersection from the primitive range is present inthe resultQueue, whether one or more alpha intersections are present inthe result queue, whether the resultQueue is full, whether additionalalpha intersections were found in the primitive range that have not beenreturned to the SM and which are not present in the resultQueue, and theindex of the next alpha primitive in the primitive range for the ray totest after the SM consumes the contents of the resultQueue (the index ofthe next primitive in the range after the alpha primitive with thehighest memory-order from the current primitive range in the resultqueue).

When the Stack Management Unit 740 receives the packet from IntersectionManagement Unit 722, the Stack Management Unit 740 inspects the packetto determine the next action required to complete the traversal step andstart the next one. If the packet from Intersection Management Unit 722indicates an opaque intersection has been found in the primitive rangeand the ray mode bits indicate the ray is to finish traversal once anyintersection has been found the Stack Management Unit 740 returns theray and its results queue to the SM with traversal state indicating thattraversal is complete (a done flag set and/or an empty top level andbottom level stack). If the packet from Intersection Management Unit 722indicates that there opaque or alpha intersection in the result queueand that there are remaining alpha intersections in the primitive rangenot present in the result queue that were encountered by the ray duringthe processing of the primitive range that have not already beenreturned to the SM, the Stack Management Unit 740 returns the ray andthe result queue to the SM with traversal state modified to set the cullopaque bit to prevent further processing of opaque primitives in theprimitive range and the primitive range starting index advanced to thefirst alpha primitive after the highest alpha primitive intersectionfrom the primitive range returned to the SM in the ray's result queue.If the packet from Intersection Management Unit 722 indicates that noopaque or alpha intersections were found when the ray processed theprimitive range the Stack Management Unit 740 pops the top of stackentry (corresponding to the finished primitive range) off the activetraversal stack. If the packet from Stack Management Unit 740 indicatesor that either there are opaque intersections in the result queue andthe ray mode bits do not indicate that the ray is to finish traversalonce any intersection has been found and/or there are alphaintersections in the result queue, but there were no remaining alphaintersections found in the primitive range not present in the resultqueue that have not already been returned to the SM the Stack ManagementUnit 740 pops the top of stack entry (corresponding to the finishedprimitive range) off the active traversal stack and modifies thecontents of the result queue to indicate that all intersections presentin the result queue come from a primitive range whose processing wascompleted.

If the active stack is the bottom stack, and the bottom stack is emptythe Stack Management Unit 740 sets the active stack to the top stack. Ifthe top stack is the active stack, and the active stack is empty, thenthe Stack Management Unit 740 returns the ray and its result queue tothe SM with traversal state indicating that traversal is complete (adone flag set and/or an empty top level and bottom level stack). If theactive stack contains one or more stack entries, then the StackManagement Unit 740 inspects the top stack entry and starts the nexttraversal step. Testing of primitive and/or primitive ranges forintersections with a ray and returning results to the SM 132 aredescribed in co-pending U.S. application Ser. No. ______ entitled“Conservative Watertight Ray Triangle Intersection” (Atty. Dkt. 6610-36(18-SC-0145)), U.S. application Ser. No. ______ entitled “Method forContinued Bounding Volume Hierarchy Traversal on Intersection withoutShader Intervention” (Atty. Dkt. 6610-32 (18-AU-0127)) and U.S.application Ser. No. ______ entitled “Method for Handling Out-of-OrderOpaque and Alpha Ray/Primitive Intersections” (Atty. Dkt. 6610-37(18-AU-0149)), which are hereby incorporated by reference in theirentireties.

While the above disclosure is framed in the specific context of computergraphics and visualization, ray tracing and the disclosed traversalcoprocessor could be used for a variety of applications beyond graphicsand visualization. Non-limiting examples include sound propagation forrealistic sound synthesis, simulation of sonar systems, design ofoptical elements and systems, particle transport simulation (e.g., formedical physics or experimental high-energy physics), general wavepropagation simulation, comparison to LIDAR data for purposes e.g., ofrobot or vehicle localization, and others. OptiX™ has already been usedfor some of these application areas in the past.

Streaming Scheduling Cache Memory

In a ray tracer, each ray traverses the acceleration data structure(e.g., BVH) in an individualistic way. It might at first appear thatrays shot into a given scene would be relatively coherent because theyall emanate from the same viewpoint. However, this may be true only atthe beginning of a typical ray tracing process. As the ray tracingprocedure progresses, coherence diverges with traversal and shading. Itusually takes multiple steps to determine which objects are visible,which light reaches which objects, and which surfaces reflect where. Asthat divergence spreads in the rays that are being processed, theoccupancy of execution on banks of single instruction multithreadedengines used by some ray tracers diminishes very rapidly. In such SIMTray tracer architectures, only a small portion of SIMT processors willbe doing work at any given time with the rest of the processors becomingidle. In some embodiments, a streaming multiprocessor executes on awarp, so as the threads diverge, the number of active threads drops butthe number of warps being processed on various streaming multiprocessorsdoes not necessarily drop until the final tail. Accordingly,improvements are possible.

Ray tracing often involves executing a ray intersection query against apre-built Bounding Volume Hierarchy (BVH). Individual rays in thosequeries often take similar paths through the hierarchy. Grouping theircoherent execution together can have performance and power benefits.Previous implementations outside of the Tree Traversal Unit (TTU) havegenerally not grouped that execution together and duplicate BVH nodesinto register files rather than operating directly out of the cacheusing a single instance of data.

Streaming Cache Memory

The present example non-limiting embodiments provide a traversalcoprocessor cache memory designed to be configurable, small, andefficient at streaming workloads. It is also a cache memory which doesnot need to return to the requesting client, but rather schedulesexecution down an attached data path using request metadata and dataresident in the cache memory itself. In this manner, the cache memory isnot a typical cache memory, but also functions as a scheduler which usesresident data to appropriately and efficiently group ray requestoperation/execution.

In example non-limiting embodiments, the acceleration data structure isstored in compressed form in memory. As a ray makes its way through thehierarchy the acceleration data structure defines, the TTU 700 willretrieve the necessary data from memory via a cache line of a L1 cachememory. The TTU 700 will then test the ray against the data that hasbeen retrieved and made available in the cache line.

To facilitate this process, TTU 700 has its own internal small butefficient streaming cache 750 here called an “L0 cache” (“L zero cache”or “level zero cache”). In the non-limiting example shown in FIG. 9, theL0 cache is within the TTU 700 itself. This TTU L0 cache 750 is backedby a larger, more powerful memory system including an additional L1cache (“level one cache”) and possibly other cache levels such as alevel 2 cache etc. ultimately providing access to main memory 140 (seeFIG. 1). In the example non-limiting embodiment, L0 cache 750 is usedonly by and is dedicated to TTU 700. This L0 cache 750 pulls in data foruse by the TTU 700 and also schedules use of that data against the raysthat want to test against it. The cache 750 performs its schedulingfunction implicitly through the order in which it streams data down thedata path to the other parts of TTU 700.

Background—Basic Cache Memory Concepts

As those skilled in the art understand, conventional “cache memory” hasbeen used in high speed computer architectures for many years. The basicidea behind a cache memory is to place a small, easily-accessible memoryclose to a high speed processor, typically on the same silicon. Theprocessor issues requests for data from the main memory by sending themthrough the cache memory. The cache memory retrieves the requested datafrom main memory, and stores the retrieved data in a small local memorythat the processor can access more quickly.

A typical cache memory retrieves, stores and maintains data that anexecuting process needs to run. Retrieval into a typical cache memory isinitiated by a process calling for that data from main memory. Butinstead of simply returning the retrieved data to the process, the cachememory also maintains a copy of the data in local memory close to theprocess that is using the data. If the process needs the same data again(which it often may, due to a phenomenon known as “localizedexecution”), the cache memory can provide it quickly without having toretrieve it again from main memory. When the executing process no longerneeds the data (e.g., because it has made forward progress to anotherpart of the process), the data can be evicted from the cache to makeroom for other data the process now needs.

Home cooks will be familiar with the concept of a cache, since a typicalhome refrigerator constitutes a kind of a food cache. There are vastamounts of different ingredients in the refrigerated section of yourlocal food supermarket, but having to go all the way to the store eachtime you need an ingredient would be very burdensome. Instead, the homecook occasionally brings home from the supermarket the ingredientslikely to be needed in the next few days, and stores them in a homerefrigerator. The home refrigerator is just a few steps from the sinkand stove, so the ingredients it contains are easy for the cook toaccess quickly. The cook will need to replenish the contents of the homerefrigerator periodically. And the cook may sometimes need to run to thestore to pick up special ingredients that the home refrigerator does notcontain.

A typical advantage of a cache memory is thus reduced latency—the timeit takes to retrieve data from memory. It is usually much quicker for aprocess to obtain data from a local cache than to retrieve it frommemory. Because many processes tend to reuse the same data over and overagain (“locality of reference”), it can be quite efficient to maintain alocal copy of data that the process can access much more quickly ascompared to retrieving it from shared main memory.

The Traversal Processor Cache Memory Also Functions as a Scheduler

If TTU 700 memory latency (i.e., the amount of time it takes to retrievedata from the memory system) were the only constraint, a possiblesolution would be to provide a large dedicated L0 cache for TTU 700 infront of the L1 cache that the TTU 700 shares with the SMs 132. However,for purposes of efficiency and to save hardware area, a better designprovides TTU task scheduling.

In the example non-limiting embodiments, the TTU 700 relies on and isbacked by the provided memory system including a larger L1 cache that isshared between one or more SMs 132, TTU 700, and texture mapping units.In addition however, the TTU 700 is provided with its own, very smalland highly efficient streaming L0 cache 750 that caches data retrievedfrom the L1 cache and also schedules ray execution through its datapath.

Ray Operation Scheduling

To provide high efficiency, the example non-limiting embodiment L0 cache750 provides ray execution scheduling via the data path into the cacheitself. In example non-limiting embodiments, the cache 750 performs itsray execution scheduling based on the order in which it fulfills datarequests. In particular, the cache 750 keeps track of which rays arewaiting for the same data to be returned from the memory system andthen—once it retrieves and stores the data in a cache line—satisfies atabout the same time the requests of all of those rays that are waitingfor that same data.

The cache 750 thus imposes a time-coherency on the TTU 700's executionof any particular collection of currently-activated rays that happen tobe currently waiting for the same data by essentially forcing the TTU toexecute on all of those rays at about the same time. Because all of therays in the group execute at about the same time and each take about thesame time to execute, the cache 750 effectively bunches the rays intoexecuting time-coherently by serving them at about the same time. Thesebunched rays go on to repetitively perform each iteration in a recursivetraversal of the acceleration data structure in a time-coherent mannerso long as the rays continue to request the same data for eachiteration.

The cache 750's bunching of rays in a time-coherent manner by deliveringto them at about the same time the data they were waiting on,effectively schedules the TTU 700's next successive data requests forthese rays to also occur at about the same time, meaning that the cachecan satisfy those successive data requests at about the same time withthe same new data retrieved from the memory system.

An advantage of the L0 cache 750 grouping rays in this way is that theresulting group of ray requests executing on the same data take roughlythe same traversal path through the hierarchical data structure andtherefore will likely request the same data from the L0 cache 750 atabout the same time for each of several successive iterations—eventhough each individual ray request is not formally coordinated with anyother ray request. By the fact that the L0 cache 750 is scheduling viathe data path to TTU blocks 710, 712, 740, the L0 cache is effectivelyscheduling its own future requests to the memory system on behalf of therays it has bunched together in order to minimize latency whileproviding acceptable performance with a relatively small cache data RAMhaving a relatively small number of cache lines. This bunching also hasthe effect of improving the locality of reference in the L1 cache andany downstream caches.

If rays in the bunch begin to diverge by requesting different traversaldata, cache 750 ceases serving them at the same time as other rays inthe bunch. The divergence happens as the size of the bounding boxes inthe BVH decreases at lower levels. What might have been minute,ignorable differences in origin or direction early on, now cause rayspreviously bunched to miss or hit those smaller bounding boxesdifferently.

Hierarchical Data Structure Traversal—how Ray Operations are Activated

In the example non-limiting embodiment, the TTU L0 cache 750 feedscomplets to a data path at the end of which is a stack management block740. The stack management block 740 in the example non-limitingtraversal coprocessor architecture determines what operation isperformed next for a given ray. Thus, the stack management block 740determines whether recursion within the acceleration data structure isrequired (e.g., to further subdivide a bounding volume into its childrenfor further successive ray-complet tests), or whether a leaf node hasbeen reached in the acceleration data structure (in which case the stackmanagement unit will schedule a ray-triangle test to be performed by theray-triangle test block 720). If the stack management block 740determines that additional recursion down the bounding box path isrequired, then the stack management block 740 will initiate a furtherrequest to the L0 cache 750 to retrieve the next complet in thehierarchical data structure required for a next successive iteration ofthe ray-complet test by block 710.

The complet cache 752 is thus in line in the data path. It is part of anexecute-request-execute-request loop that, because it is within the datapath, has information about which complets have been retrieved and areavailable and which complets result in cache misses that requireadditional memory accesses to the memory 140.

Additionally, by grouping the requests together so that many ray-complettests that are tested against the same complet data are scheduled to beperformed at more or less at the same time, rays that are“coherent”—meaning that they are grouped together to perform theirray-complet tests against the same complet data—will remain groupedtogether for additional tests. By continuing to group these raystogether as a bundle of coherent rays, the number of redundant memoryaccess to retrieve the same complet data over and over again issubstantially reduced and therefore the TTU 700 operates much moreefficiently. In other words, the rays that are taking more or less thesame traversal path through the acceleration data structure tend to begrouped together for purposes of execution of the ray-complet test—notjust for the current test execution but also for further successive testexecutions as this bundle of “coherent” rays continue their way down thetraversal of the acceleration data structure. This substantiallyincreases the efficiency of each request made out to memory byleveraging it across a number of different rays, and substantiallydecreases the power consumption of the hardware.

In order to keep continually feeding the ray-complet test blocks 710with new data, ideally the complet cache 752 should be providing newcomplet data every cycle. Expecting a more distant L1 cache to providesuch high responsivity may be unrealistic. To the contrary, the moredistant L1 cache could end up being swamped and become a bottleneck forthe ray-complet tests that the TTU 700 is able to perform at extremelyhigh rates. In the example non-limiting embodiment, the L1 cache isshared between the TTU 700, one or more SMs 132 and one or more texturemapping units. The L0 cache 750 within the TTU 700 thus provides abuffering effect so that memory access is by the TTU or offloaded to theTTU's own L0 cache, and those memory accesses do not interfere withmemory accesses the SMs 132 and texture mapping units may be performingvia the shared L1 cache. The L0 cache 750 within TTU 700 thus providesbandwidth amplification without substantial additional complexity to thememory system. The TTU's L0 cache 750 has the effect of reducinglatency, and increasing bandwidth as a result of hitting the L0 cacherather than going out to the L1 cache or even to main memory.

Great advantages are obtained by the ability of the L0 caching structure750 to group ray execution based on the data the grouped rays require totraverse the acceleration data structure. The SM 132 that presents raysto TTU 700 for complet testing, in a general case may have no idea thatthose rays are “coherent.” The rays may exist adjacent to one another inthree-dimensional space, but typically traverse the acceleration datastructure entirely independently. Whether particular rays are thuscoherent with one another depends not only on the spatial positions ofthe rays (which SM 132 can determine itself or through other utilityoperations such as even artificial intelligence), but also on thedetailed particular acceleration data structure the rays are traversing.Because the SM 132 requesting the ray-complet tests does not necessarilyhave direct access to the detailed acceleration data structure (notwould it have the time to analyze the acceleration data structure evenif it did have access), the SM relies on TTU 700 to accelerate the datastructure traversal for whatever rays the SM 132 presents to the TTU fortesting. In the example non-limiting embodiment, the TTU 700's own L0cache 750 provides an additional degree of intelligence that discovers,based on independently-traversing rays requesting the same complet dataat about the same time, that those rays are coherently traversing theacceleration data structure. By initially grouping these coherent raystogether so that they execute ray-complet tests at about the same time,these coherent rays are given the opportunity again to be groupedtogether for successive tests as the rays traverse the acceleration datastructure. The TTU L0 cache 750 thus does not rely on any predeterminedgrouping of rays as coherent (although it does make use of a naturalpresentation order of rays by the requesting SM 132 based simply onspatial adjacency of the rays as presented by the SM for testing), butinstead observes based on the data the rays require for testing as theytraverse the acceleration data structure that these rays are traversingthe same parts of the acceleration data structure and can be groupedtogether for efficiency.

Ray Activation

In general, anything the TTU 700 performs in the example non-limitingimplementation will be on behalf of a ray. Thus, the L0 cache 750 is,generally speaking, performing tasks on behalf of rays as opposed toperforming other memory retrieval tasks. Thus, all requests from the TTUL0 cache 750 to the memory system including the L1 cache will be onbehalf of a ray. In the example non-limiting embodiment, the parametersthat determine a ray include for example origin, direction, tmin andtmax. These parameters are set explicitly by the SM 132 in its requestto the TTU 700, rather than being loaded from memory. What is loadeddirectly for use by TTU 700 are complets, triangles and instance nodes.The complet in the example non-limiting embodiment represents boundingboxes and associated pointers. The complet is stored in a compressedform in memory 140 and the cache 750 retrieves it from the memory systemin that compressed format.

In example non-limiting embodiments, the stack management unit 740 iswhat updates the TTU stack as rays recursively traverse down and thenback up the acceleration data structure. It is the stack management unit740 that determines, for example, whether a particular ray requiresanother recursive traversal through the accelerated data structure, orwhether the ray now requires for example a ray-triangle test using theother data path. In the example non-limiting embodiment, the stackmanagement unit 740 may choose which ray to activate next on around-robin basis, which is essentially a random choice with respect toany particular time-coherence of data requirements of that particularray. A randomly-chosen ray is thus selected to pass down the ray-completdata path, and that randomly-selected ray is what initiates a new datarequest to the L0 cache 750 for a complet that that randomly-selectedray needs for its own ray-complet test. It is the L0 cache 750 thatrecognizes any opportunity to group that randomly-selected ray withother rays that require the same data in order to make the memoryaccesses by TTU 700 more efficient. Accordingly, the L0 cache 750 willimpose a group order of execution on coherent rays that are traversingsimilar paths through the acceleration data structure even though theformal ray execution scheduling process the TTU 700 performs via thestack management unit 740 and the complet scheduling are actuallyselecting rays for activation based on what may be a random selectionprocess.

In the example embodiments, each ray starts its traversal of theaccelerated data structure from a place at which an SM 132 asked it tostart. The L0 cache 750 finds opportunities to coalesce rays behindspecific blocks of memory. As more rays need that same block of memorywhile a pending request is being satisfied, the L0 cache 750 pushesthose new requests into a pending request table. Once a particular cacheline is retrieved from main memory and is valid, all of the rays thatare waiting for that data can then drain into the data pipe.

Since any request to memory processed by the L0 cache 750 is initiatedon behalf of a particular ray, any retrieval from higher level memoryinto the L0 cache is also with respect to a particular requesting ray.For example, the first ray that asks for a particular complet frommemory will initiate a request by L0 cache 750 to retrieve that completfrom higher level memory. This request on behalf of a particular ray isinitiated in the example non-limiting embodiment by the stack managementunit 740 in potential cooperation with the complet scheduler based onthe rays that are available for activation.

The stack management unit 740 picks a ray for activation, and thatselected ray activates and initiates a request for a complet via the L0cache 750. In some cases, the stack management unit 740 may determinethat the request on behalf of that particular ray is not for a completbut is instead to make use of the other data path to perform aray-triangle test and/or an instance transform.

The stack management unit 740 will independently repeat this sameprocess for the next ray that is available for activation. That next rayis independently activated and independently generates its own memoryrequest via the L0 cache 750. However, in the example non-limitingembodiment, the L0 cache 750 uses its internal state information todetermine whether the cache has already sent a request for that samedata to higher level memory. Of course, if the L0 cache 750 already hasretrieved that same data and that data is available in a cache line ofdata RAM 1208 (see below), the L0 cache may provide it to the requestingprocess within the TTU 700. However, in this case assume that the L0cache 750 has made a request for that same data on behalf of another rayand is not yet received the data back from high level memory so the datais not yet available and valid in a cache line. At this point, the L0cache 750 registers the new request and effectively links or groups theray making the new request to the ray that made the previous requestbecause both rays are requesting the same data.

The L0 cache 750 can link or group any number of rays together in thismanner Once the data is retrieved and is valid in a cache line, the L0cache 750 can serve all of those grouped rays the data at the same orabout the same time. This scheduling provides higher levels ofefficiency even though the only rays in the example non-limitingembodiment that ever receive particular data from the L0 cache 750 arethose rays that have asked for that particular data.

Thus, in example non-limiting embodiments, a ray will come into thecache designating a complet that the ray is supposed to test against.Typically, a complet will be retrieved on a single cache line. The TTU700 in response to the ray-complet test 710 makes a request out tomemory 140 to retrieve the complet into the L0 complet cache 752 so itcan be available for the ray-complet test block 710 for that ray. Itturns out that other rays may want to test against that very samecomplet data.

To enable this sharing of the retrieved data and minimize unnecessaryadditional memory retrievals, the example non-limiting embodiment groupsrays using a common pending address table (PAT) 1206 entry (see below).When that complet data is retrieved into the L0 complet cache 752, thecomplet cache 752 essentially schedules the ray-complet tests for all ofthe rays that have been waiting on that complet data by servicing theray-complet test block 710's memory access requests all at the same timeor about the same time. These operations could in some exampleimplementations operate simultaneously in parallel, but in the exampleshown in FIG. 9 there may be only a limited number of (e.g., two)ray-complet test blocks 710, so some scheduled ray-complet tests may beperformed simultaneously whereas others may be scheduled to be performedseriatim (e.g., back to back) but still close in time (e.g.,back-to-back) to one another. This scheduling is performed as part ofthe data path (i.e., simply by the cache 750 providing the data forthese now-grouped rays in a time-coherent manner) rather than requiringintervention by an SM 132, an explicit ray scheduler, or other return toa client.

TTU 700 may provide a complet scheduler but the scheduling performed bycache 750 may be transparent to that complet scheduler and simplyoptimizes the performance of the cache with respect to whatevercollection of rays the complet scheduler schedules for execution. Inparticular, the cache 750 will impose a time-coherency on execution ofrays that need the same data simply by servicing their individualindependent data requests at about the same time, and then continuedoing so from traversal iteration to traversal iteration in order toenhance the efficiency of the cache in servicing data requests for allrays TTU 700 is currently handling. The objective is not to speed up thememory performance or execution associated with any particular ray, butrather to group together execution of rays that happen to be traversingthe same parts of the accelerated data structure so that streaming cache750 can service all such rays with the same data the cache obtains fromthe memory system (e.g., a L1 cache) to reduce latency while minimizingthe size of the streaming cache.

TTU Queries Provide Initial Groupings of Ray Operations

The L0 cache 750 can be effective in grouping rays in this manner bytaking advantage of time-coherence of the traversal of particular partsof the accelerated data structure of multiple rays the SM's 132 presentto TTU 700. This time-coherence starts with the query itself. A query toTTU 700 from an SM 132 will typically consist of a number of rays (e.g.,32 different rays in some embodiments). This means that TTU 700 willstart off a certain number of rays at the same point of the accelerateddata structure (e.g., the root node of the bounding volume hierarchytree). These initial rays are “primary” rays, i.e., they are typicallystarting from a viewpoint position and are being shot into the threedimensional scene in often similar directions. These rays will oftentake similar initial paths through the hierarchical data structurebecause they are emanating from the same viewpoint position within thescene and are shot into the scene in similar directions. They are notguaranteed to take similar traversal paths, but they are likely to doso.

The TTU L0 Cache Preserves and Exploits the Initial Ray OperationGrouping

Since the SM 132 initially groups these rays for being launched into theTTU 700 at the same time, the TTU sends the complet request for theserays to the L0 cache 750 back-to-back. The L0 cache 750 thus sees thesecomplet requests from these various rays come in more or less as agroup, preserves this grouping when memory values are retrieved from thehigher level of memory, and preserves this grouping from iteration toiteration as the rays continue to traverse the accelerated datastructure.

If the rays begin to diverge (i.e., they are no longer taking the samepath through the data hierarchy anymore because they begin hittingdifferent objects or other portions of the hierarchy), then the groupingbeing attempted by the L0 cache 750 becomes less effective. Suchdivergence will result in singletons, where there is only ray in a groupthat is waiting on a particular complet to be retrieved from the higherlevels of memory. For primary ray traversal, the singleton ratio isrelatively low. However, when modeling reflection where the primary rayshave been reflected off of different surfaces of the scene and arebouncing through the scene in random directions from the various objectsin the scene, the traversal becomes much less coherent and the ratio ofsingletons to grouped rays increases. However, even when this happens,because all rays are still traversing the same BVH, all rays will stillbegin their traversals at the top of the tree and thus require at leastthe same initial complet. Thus, even in the case of secondary orreflected ray traversal, the L0 cache 750 may still be able to takeadvantage of some coherence between grouped rays when they aretraversing near the top of the BVH hierarchical data structure. As thesesecondary rays begin traversing downwards into the hierarchical datastructure, the coherence begins to be lost and the ratio of singletonsbecomes much higher.

The TTU L0 Cache Discovers Ray Operations that Follow the Same orSimilar Traversal Paths Through a Bounding Volume Hierarchy

In the example non-limited embodiment, it is the grouping in the L0cache 750 itself that encourages coherence, not any explicit schedulingby the stack management unit 740 or even by SMs 132. When the SM queriesstart with some coherence, the L0 cache 750 tends to recognize andpreserve the coherence and uses it as an opportunity for increasingmemory access efficiency. But the L0 cache 750 is able to discovercoherence independently of coherence contained within the SM 132queries. For example, the L0 cache 750 is able to discover datacoherence for rays that are all accessing the top of the hierarchicaldata structure even if the eventual paths those rays will take throughthe data structure will be widely divergent and incoherent.

In the example non-limiting embodiment, each SM 132 executes amultiplicity of threads such as 32 threads, each thread managing aparticular ray. If the SM 132 is executing 32 threads, its query to theTTU 700 will comprise a batch of 32 rays. Once those 32 rays are insidethe TTU 700, they are processed independently from one another until itis time to return the results from the TTU back to the requesting SM132. The TTU 700 can process those rays in any order with any staging,the main timing constraint being that the TTU sends the requesting SM132 reports of ray traversal results in the same grouping as theoriginal requests.

In the example non-limiting embodiment, there are multiple SMs 132 andin some embodiments, a single TTU 700 serves plural (e.g., two) SMs 132.In the example non-limiting embodiment, the TTU 700 processes rayrequests from these two different SMs 132. These SMs 132 do notcoordinate their respective ray requests in terms of time coherence, butthe TTU L0 cache 750 may discover that rays launched by one SM 132 arefollowing the same traversal path as rays launched by another SM, andgroup ray requests from different SM's together for purposes of makingretrieval of data for all of those rays more efficient over a successionof data structure traversal iterations. The L0 cache 750 is in theexample non-limiting embodiment thus able to recognize coherence betweenrays in different queries from different SMs 132 and service thosequeries more efficiently based upon the L0 cache 750 exploitingcoherence between those different rays—coherence which the SMs 132themselves did not recognize or inform the TTU 700 about. In the examplenon-limiting embodiment, there may be hundreds of rays that are activeat one time, and it is up to the TTU to service all of those rays asefficiently as possible. The L0 cache 750 thus effectively acts as ascheduler by encouraging coherence as a result of the grouping the L0cache performs.

Example Non-Limiting Grouping Analogy

By way of a rough analogy, think of a cafeteria food serving linestaffed by a shortage of food service professionals. The serving line isdesigned so that different entrees are served from different servingstations: meat is served from a meat serving station, fish is servedfrom a fish serving station, and vegetarian entrees are served from avegetarian serving station. But the boss breaks the bad news to theserver: two other employees have called in sick, and only one servermust now serve from all three stations by himself Because there is ashortage of servers, it is not possible to dedicate a different serverto each serving station. Worse, each different entree selection requiresthe single food server to move to a different serving station and pickup different serving utensils.

If the diners are served in a first-come-first served order, the serverwill be constantly putting down and picking up different servingutensils and moving between the serving stations. Suppose the firstdiner orders meat and the server is standing at the vegetarian station.The server has to put down the vegetarian serving utensil, move to themeat station, pick up the meat serving utensil there, and serve thediner meat. The next diner in line might also want meat, which theserver can serve very efficiently because he is already at the meatstation. But suppose the next diner wants fish. The server must put downthe meat serving utensil, move to the fish station, pick up the fishserving utensil and satisfy the diner's request for fish. Meanwhile, theserving line is getting longer and longer, with hungry diners anxious toget their meals.

The server then gets a bright idea. He calls out to the diners and asksthem to form three different lines: one for meat, a second for fish anda third for vegetarian. The server serves meat to the first ten dinersin the meat line in quick succession; then moves to the fish station toserve the first ten diners who are waiting for fish; and then moves tothe vegetarian station to serve vegetarian meals to the diners waitingthere. By grouping diners based on the entrees they are waiting for, theserver has increased his overall efficiency and has effectivelyscheduled when the diners are served (meat eaters are served, thenseafood eaters, then vegetarians, and so on). Individual diners at thehead of the line might wait longer to get their individual meals than ifeveryone were served in first-come-first-served order, but the overalleffect is that all diners will on average be served faster and moreefficiently by a less frantic food server.

Taking the analogy above further, suppose all diners are entitled to getsecond helpings. Knowing this, the server modifies his strategy. Forfirst helpings, the server now decides to serve all vegetarians in theline as soon as the vegetarian food tray arrives from the kitchen, andonly after all vegetarians have been served does he change stations. Hethen serves all seafood eaters before serving any meat eaters. Becausethe vegetarians were all initially served at about the same time, theywill all come up for their second helpings at about the same time. Thismeans that based on the timing of when the diners in the cafeteriapresent their requests for seconds, the server will be able to follow asimilar strategy of serving all of the vegetarians their second helpingsfrom the vegetarian station, then serving all the fish eaters theirsecond helpings together from the fish serving station, and finally,serving all meat eaters their second helpings together from the meatserving station—minimizing the number of times the server needs tochange from one serving station to another even though the server hasnot explicitly controlled when any diner is going to be served seconds.While the server is happy to accommodate diners who ordered fishinitially but now want vegetarian seconds, the server has effectivelyimposed a time-grouping on the diners based on their initial requests sothey will naturally present themselves for seconds in a time-coherentway—thereby further increasing the efficiency of the server to serve theentire meal including seconds, thirds—and in the case of the TTU 700with a large acceleration data structure—on and on.

Example More Detailed Structure & Implementation of Streaming CacheMemory 750

FIG. 12 shows an example non-limiting embodiment of an implementation ofcache memory 750. In this example, the structure of cache memory 750comprises a pending request table (PRT) 1202, a tag structure (TAG)1204, a pending address table (PAT) 1206, a data RAM (DAT) 1208 andlogic circuitry 1210.

The logic circuitry 1210 allocates a PRT table 1202 entry to eachrequest to the cache 750. Each PRT table 1202 entry holds onto requestmetadata and has a pointer to a single PAT 1206 entry. In the examplenon-limiting embodiment, every request that is presented to the L0 cache750 receives an entry in the pending request table 1202. The pendingrequest table 1202 is thus used to distinguish unique requests. The PRTtable 1202 entry remains valid until the cache satisfies the request bysending the data for that request on to the attached data path.

All requests to the cache 750 check the tags (TAG) 1204 for a previousvalid entry at the same address and context. The TAG 1204 can bemulti-way, set-associative or direct mapped as will be understood bythose skilled in the art. For example, as shown in FIG. 12, the TAG 1204may comprise an entry corresponding to each data line of the data RAM1208, the tag containing the main memory address (“ADDR”) of the datablock stored in the cache line, an offset or pointer (“OFF”) into thedata RAM 1208 indicating which of the data lines stores the data blockretrieved from that memory address, and a valid (“V”) bit indicatingwhether the data in the corresponding cache line is valid. In theexample non-limiting embodiment, the cache memory 750 is read-only, sothere is no need for a “dirty” bit in the TAG 1204 that would indicate aneed to write modified data back into main memory, but in otherembodiments such a “dirty” bit and a writeback capability could beprovided.

Hit and Miss Detection

As shown in FIG. 13, depending on state, the result of a TAG 1204 checkof a new address request to cache 750 is either:

(1) data hit with no previous requests to the same data resident in thecache (“N” exit to decision block 1256),

(2) data hit with previous request(s) to the same data resident in thecache (“Y” exit to decision block 1256),

(3) data miss with previous request(s) to the same data resident in thecache (“Y” exit to decision block 1258), or

(4) data miss with no previous currently tracked requests to the samedata resident in the cache (“N” exit to decision block 1258).

Here, a data “hit” means that valid data responsive to the request isalready resident in data RAM 1208. A data “miss” means that valid dataresponsive to the request is not already resident in data RAM 1208 andmust be retrieved from higher levels of memory. A “hit” or “miss” istypically determined by comparing the memory address of the incomingrequest against memory addresses stored in the TAG 1204.

In cases 1 and 4, a tag 1204 entry is allocated (see below) (blocks1262, 1266), possibly evicting a previous tag entry. A tag entry remainsvalid until it is either evicted or the associated PAT 1206 entry anddata RAM 1208 entry are invalidated. That is, a tag entry need not bevalid for the length of the request that created it. Some exampleimplementations might tie the TAG 1204 and PAT 1206 entries together. Inthat case, the tag entry remains valid while the PAT entry is validand/or the data RAM is valid.

A PAT 1206 entry is allocated only in cases 1 and 4 above (blocks 1260,1264). The PAT 1206 entry contains all information necessary to requestdata from other levels of the cache hierarchy and to send non-uniquedata down the attached data path to the rest of TTU 700.

In the example embodiment, the pending address table (PAT) 1206 createsthe unique groups for the unique addresses that are being requested fromthe other levels of memory. For each unique address, there is a pendingaddress table 1206 entry for each request that is active for thataddress within the cache 750 at any time. The tag 1204 associates anaddress with a current pending address table 1206 entry. With the tag1204 access, it is possible to have a data hit of something that isresident in the data RAM 1208 with no previous request to that sameaddress resident in the cache 750. That kind of request will allowallocation of both a PRT 1202 entry and a PAT 1206 entry.

On allocation, the TAG 1204 entry is updated with a pointer to the PAT1206 entry (blocks 1262, 1266) so that subsequent requests that hit (TAGcheck result cases 2 and 3 above) can be associated in the same group.Some implementations might tie the TAG 1204 and PAT 1206 entriestogether. Multiple PRT 1202 entries can refer to the same PAT 1206entry. The PAT 1206 entry remains valid until all associated PRT 1202entries that refer to it have been scheduled.

Groups of requests tracked in the PRT 1202 that are assigned to the samePAT 1206 entry are referred to herein as “PAT groups.” Data requests toother levels of the cache are made based on the content of the PAT 1206entry. The data that returns is stored in the data RAM (DAT) 1208. TheDAT 1208 uses an allocate-on-return policy. In one example embodiment,if there are no entries available in the data RAM 1208, the response isignored (dropped) and the cache 750 makes a new request to higher levelmemory. To guarantee forward progress, data is only dropped some smallnumber of times before all new requests are held off until the datareturns and finds a valid entry (see below).

As discussed above, another type of request is a data hit in which aprevious request of the same data is already resident in the cache. Thismeans that a previous request has already allocated a PAT 1206 entry. Inthis case, the cache 750 does not need to make an additional request,but just allows the current request to piggyback off of thepreviously-made request. In this case, the PRT 1202 entry correspondingto this most recent request is written to provide a pointer to the PAT1206 entry that has already previously been requested.

A third option for the tag is a data miss with the previous request forthe same data already present in the cache. This means that the previousrequest missed, and the cache 750 needs to send the request up to memoryin order to retrieve the data from the memory. In that case, the currentrequest simply piggybacks off of that previous request up to memory. Anew request memory is not made, and the PRT 1202 entry is simplyallocated to the previous request.

In the event of a data miss with no previous request, the request thatgenerates a miss makes a request to the memory hierarchy. It allocates aPAT 1206 entry and a PRT 1202 entry. Eventually, the data is retrievedfrom the memory and is stored in the data RAM 1208. When that happens,the PAT 1206 entry that was waiting on the retrieval of the data frommemory is activated. When this PAT 1206 entry is activated, all the PRT1202 entries associated with that PAT entry are activated as well. Thisinformation is then sent down the data path within the TTU 700. This isan example of how the TTU L0 cache 750 groups the rays for execution—bysimply having the TTU L0 cache 750 pass the various requests down thedata path to for example the ray-complet tests 710 at about the sametime.

Scheduling Ray Processing Via the Data Path

Once resident in the DAT 1208, the TAG 1204 entry, if it is stillassociated with the PAT 1206 entry for the data, is updated to point tothat data RAM entry. Subsequent requests can then hit on that dataentry.

When the data for a PAT 1206 is resident in the data RAM (either havingjust been written or already present in the case of a hit), that PAT1206 entry is activated. After winning arbitration among other activatedPAT 1206 entries, the entry is activated and sends the resident data(plus PRT metadata) from the data RAM 1208 (block 1272), drains theassociated PRT 1202 entries, and de-allocates the PAT entry. Asubsequent request that could have hit on that PAT group (which has nowbeen served), will instead start a new PAT group. In this manner, allentries in a PAT group are scheduled together.

In some implementations, there are multiple data paths attached to thecache. In that case, each path can be fed independently by different PATgroups or by members of the same PAT group spread across the data paths.

Allocate on Return & Capacity Error Handling

One of the other aspects to this TTU L0 cache 750 is that the data RAM1208 it contains operates on a principle of allocate-on-return policy.This allows the data RAM 1208 to be kept very small. So for example inone mode of operation there could be over 100 tags 1204 outstanding andthese are all satisfied using only eight data lines of data RAM 1208.When data comes back from the memory, all of those data lines arecorrectly locked because there are many pending requests trying to readthat data from the cache and those pending requests are backed up. Thecache 750 will drop the request and make a whole new request out to theL1 cache. Thus, if the allocate-on-return mechanism fails, the L0 cache750 just retries the request. In the example non-limiting embodiment,there is a limit on the number of times that any given request can beretried before the request enters a critical mode where new requests arenot allowed until the previous requests are satisfied. This prevents newrequests from coming in and starving out older requests.

As far as the L1 cache is concerned, the repeated request is acompletely distinct and new request. From the perspective of the L0cache 750, the L0 has dropped the data because there was no place tostore it. The L0 cache 750 repeats the request and sends out a newrequest to the L1 cache. However, this new request is, from thestandpoint of the L1 cache, identical to the original request and can befulfilled in the same way assuming the data is still resident in the L1cache. The only difference is that in the example non-limitingembodiment, the L0 cache 750 records that it has already previouslyrequested this same data and that this is a retry based onunavailability of space in the data RAM 1208 to store it once the L1cache has provided it. In the example non-limiting embodiment, if thisprocess is repeated too many times (in one example embodiment “too manytimes” means that it is performed a total of twice), the TTU L0 cache750 stops new requests from coming in to prevent space within the dataRAM 1208 from being occupied by new requests when older request havegone unfulfilled. This allows the older requests to make forwardprogress.

For example, suppose that there are 64 pending requests to the L0 cache750. Assume that all of those requests are requesting the same address.The TTU L0 cache 750 makes just one corresponding request to the L1cache to fulfill all 64 requests for the same data. When that datareturns from the L1 cache and is written into the data RAM 1208 of theL0 cache 750, there will be 64 requests that the L0 cache will schedulefor execution based on the return of that data. The rate at which theexample non-limiting embodiment schedules ray tests for execution can beone per cycle. It may thus take 64 cycles to drain the 64 requests outof the L0 cache 750. Other implementations are possible.

Assume now that just behind the 64 requests for the same data, therewere an additional 64 requests where each request is requesting adifferent address. Assume that the L1 cache begins responding to these64 new requests one at a time. After the L1 cache has responded to acertain number of requests (e.g., seven new requests), the data RAM 1208in the TTU L0 cache 750 will be completely full—since the data RAM mustcontinue to retain the first value as long as it is continuing toservice the 64 original requests that all use the same address. As theL1 cache continues to deliver data in response to the new requests, theTTU L0 cache 750 will determine that the data RAM 1208 is completelyfull of valid data that is awaiting usage and cannot yet be overwritten.

In the example non-limiting embodiment, there is no room in data RAM1208 for the eighth response for new data that is returned by the L1cache, so the L0 cache drops the response and makes a new request forthe same address back to the L1 cache. In this case, the back pressurein the L0 cache 750 is being created by one or typically a small numberof data valid conditions in which the L0 cache is continuing to deliverthe same data to a relatively large number of ray requests each of whichhave requested the same data, but the TTU 700 is unable to perform allof those requests at the same time and is instead relying on the L0cache to retain the data while the TTU works on the various large numberof requests that use this data. In this case, the data valid cache linesproviding data in common to a large number of requests remains lockedlonger than would be ideal—tying up the cache line to give the TTU 700time to perform the various requests that use that data.

Reconfigurable Streaming Cache

In one example non-limiting embodiment, the data ram 1208 has N lineseach of which comprises M bytes of data, for a total data RAM size ofN×M bytes. The tag 1204 and PAT table 1206 each contain X entries, andthe PRT 1202 has Y entries. This means that up to Y requests can beserviced by the L0 cache 750 at any given time, but a maximum of only Xrequests can be outstanding to the L1 cache at any given time. In thisimplementation, a maximum of N retrieved blocks of data are resident inthe data RAM 1208 at any given time.

In the example non-limiting embodiment, the sizes of the PRT 1202, TAG1204, PAT 1206, and DAT 1208 are all independently configurable. Evendown to single entry sizes are allowed. For example, a single-entry TAG1204 would only group back-to-back requests to the same address. Or asingle-entry DAT 1208 would allow only a single data line to be actedupon at any time. In non-limiting embodiments, each of these parameterscan be configured completely independently. For example, it would bepossible to configure the L0 cache 750 to have a single tag entry with64 data RAM entries and 64 PRT entries. The ratio of sizes also matters,with the PRT/PAT ratio potentially determined by the average PAT groupsize.

The ability to size the cache 750 to be very small, while stillmaintaining acceptable performance, allows for good area savings. Byincluding a scheduler into the cache design, requests with similarexecution requests can be grouped together, which can improveperformance.

For example, in one embodiment TTU 700 may be capable of handlinghundreds of rays at the same time, and yet the streaming cache 750 mightbe very small (e.g., 8 cache lines). Keeping the streaming cache 750small conserves chip area and power consumption without increasinglatency by taking advantage of inherent coherence properties ofindependent parallel rays that start from the same viewpoint and happento be traversing the same portions of the BVH acceleration datastructure.

Alternative Example Non-Limiting Implementations

While it might be possible in some example non-limiting implementationsto use the same cache for scheduling by the TTU 700 of ray-complet testsas is used for retrieval of other data by SMs 132, texture mappers andthe like, a separate dedicated L0 complet cache 752 within TTU 700provides reduced latency and is therefore helpful to increase the speedat which the TTU 700 accelerates the ray-complet test 710.

It might for example be possible to make such request using the L1cache. The L1 cache would provide the requested information, some inorder of received requests (if the data was already available within theL1 cache) and some out of order (e.g., in the event of a cache miss thatrequires retrieval from memory 140). But in such an arrangement, thereturn of the data from the L1 cache would happen in response to everyrequest that is made. This would require returning data on a data linefor every line for every request that is made. In contrast, the examplenon-limiting embodiment provides a return of complet data from thecomplet cache 752 just once for each group of rays that are requestingand need the data for the ray-complet tests to be performed againstthose rays. This substantially conserves both power and access time.

Allocating Cache Lines Upon Return

Additional features of this example L0 TTU cache 750 are noteworthy. Intraditional caches of prior systems, misses cause cache lines to beallocated. In contrast, in the example non-limiting embodiment, there isa table of requests that are allocated upon a miss. Here, cache linesare not allocated upon a miss but instead are allocated upon a returnfrom the L1 cache.

When a data line comes back, and data is now available in the data RAM1208, the PAT entry 1206 is activated at that time. It could be thatthere are multiple PAT 1206 entries that are activated and are ready togo. In the example non-limiting implementation, the TTU L0 cache 750selects between such activated and ready-to-go entries of the PAT table1206 using a round robin methodology. In the example non-limitingembodiment, there is no oldest type and first return policy. Rather, theTTU L0 cache 750 services requests to the TTU using a round robinalgorithm that also takes into account service between groups—since whenthe L0 cache begins servicing a particular PAT 1206 entry it willprovide all of the various processes that require that particularaddress before selecting another PAT entry to provide to the TTU. In theexample non-limiting embodiment, each retrieval from the L1 cache willrelate to only a single PAT 1206 entry.

In the example non-limiting embodiment, there may be two variants to howthe PAT entries are implemented. In one example implementation, it ispossible to tie a PAT 1206 to a tag 1204 line. In another exampleimplementation, a PAT 1206 entry is not tied to a tag 1204 line. In thecase that the PAT 1206 entry is tied to a tag 1204 line, the tag is keptvalid for as long as the PAT 1206 entry is valid. This creates thepotential for back pressure on the tag side. This will happen if a tagis full of lines that are for PAT 1206 entries. In that case, each PAT1206 entry is really a unique address. This is because in the examplenon-limiting embodiment, each unique address can have only a single PAT1206 entry. All of the requests for that address pending in the cachewill be grouped together.

Another example non-limiting implementation has the PAT 1206 entriesseparate from the tag 1204 entries. In such an implementation, the tag1204 entry does not need to be valid for the length of time that a PAT1206 entry is valid. In that case, it is possible for the L0 cache 750to make a request to the L1 cache which results in a fetch from the L1cache, and then an intervening request occurs. Because the tag 1204 isnot associated with the outstanding request that is still pending in thememory hierarchy, a tag can be used with the new request. This meansthat the PAT 1206 is what is used to track outstanding requests andwhether or not they have been satisfied, and the tag 1204 structure isnot used for such tracking. However, in that situation, there will be nogrouping between new requests and outstanding ones—that is, the newrequest coming into the cache 750 will not be grouped with the requestthat has already been submitted to the L1 cache and is waiting a replyfrom the L1 cache. This will result in two or more PAT 1206 entries forthe same address and the grouping is when these entered into the cache,as far as the PRT 1202 entries are assigned to each group. In thisembodiment, it is possible to have data coming back from the L1 cachethat is identical to data for the same address as a unique PAT 1206entry. Each PAT 1206 entry is going to have a single piece of data thatis coming back or is already resident in the data cache 750 in the caseof a data hit, when the tag 1204 was first accessed. In this case, eachPAT 1206 entry corresponds to data that is going to be accessed by theTTU 700.

In the example non-limiting embodiment, the L0 cache 750 is a read onlycache, and the system assumes that the data is not going to changebetween successive reads of the L0 cache. This is a reasonableassumption given that the data that is being retrieved is from anacceleration data structure that is not necessarily dynamically changedduring execution, but which is pre-stored and is simply accessed fortraversal by particular rays. If the data were subject to change, thenthe L0 cache 750 would need to invalidate the data explicitly in orderto avoid inconsistencies between successive retrievals from the samememory address.

In some example non-limiting embodiments, it may be possible to exploitthe coherence between different ray requests using a retrieval pathother than a cache. For example, in some embodiments it might bepossible to replace the L0 complet cache 752 with a buffer that makesrequests to upper levels of memory such as the L1 cache withoutassociating them with previous ray requests. In such exampleimplementation, the opportunity to exploit redundant requests forcoherent rays could be implemented using a single tag 1204implementation (which the design discussed above actually permitsthrough programming the useful size and depth of the data RAM). In thisreduced example, the only grouping available is with respect to theimmediately previous request—either the data retrieved last time aroundis the same data as has just been requested, or it is different data.This example implementation would operate less like a cache and morelike a buffer, but would nevertheless be able to exploit ray datacoherency by grouping successive ray requests that use the same dataretrieved from higher levels of memory.

It is also possible to provide an L0 cache 750 that does not directlytake advantage of this grouping aspect discussed above. Such anarrangement would reduce memory traffic to provide more efficient memoryretrieval but would not further optimize the TTU 700 operation byexploiting opportunities in scheduling ray execution based on raysneeding the same retrieved data being likely to follow the same orsimilar traversal paths through a bounding volume hierarchy. In suchimplementation, the grouping that would be performed would essentiallybe an in-order service of requests.

Example Image Generation Pipeline Including Ray Tracing

The ray tracing and other capabilities described above can be used in avariety of ways. For example, in addition to being used to render ascene using ray tracing, they may be implemented in combination withscan conversion techniques such as in the context of scan convertinggeometric building blocks (i.e., polygon primitives such as triangles)of a 3D model for generating image for display (e.g., on display 150illustrated in FIG. 1). FIG. 14 illustrates an example flowchart forprocessing primitives to provide image pixel values of an image, inaccordance with an embodiment.

As FIG. 14 shows, an image of a 3D model may be generated in response toreceiving a user input (Step 1652). The user input may be a request todisplay an image or image sequence, such as an input operation performedduring interaction with an application (e.g., a game application). Inresponse to the user input, the system performs scan conversion andrasterization of 3D model geometric primitives of a scene usingconventional GPU 3D graphics pipeline (Step 1654). The scan conversionand rasterization of geometric primitives may include for exampleprocessing primitives of the 3D model to determine image pixel valuesusing conventional techniques such as lighting, transforms, texturemapping, rasterization and the like as is well known to those skilled inthe art and discussed below in connection with FIG. 18. The generatedpixel data may be written to a frame buffer.

In step 1656, one or more rays may be traced from one or more points onthe rasterized primitives using TTU hardware acceleration. The rays maybe traced in accordance with the one or more ray-tracing capabilitiesdisclosed in this application. Based on the results of the ray tracing,the pixel values stored in the buffer may be modified (Step 1658).Modifying the pixel values may in some applications for example improvethe image quality by, for example, applying more realistic reflectionsand/or shadows. An image is displayed (Step 1660) using the modifiedpixel values stored in the buffer.

In one example, scan conversion and rasterization of geometricprimitives may be implemented using the processing system described inrelation to FIGS. 15-18, 19, 20, 21 and/or 22, and ray tracing may beimplemented by the SM's 132 using the TTU architecture described inrelation to FIG. 9, to add further visualization features (e.g.,specular reflection, shadows, etc.). FIG. 14 is just a non-limitingexample—the SM's 132 could employ the described TTU by itself withouttexture processing or other conventional 3D graphics processing toproduce images, or the SM's could employ texture processing and otherconventional 3D graphics processing without the described TTU to produceimages. The SM's can also implement any desired image generation orother functionality in software depending on the application to provideany desired programmable functionality that is not bound to the hardwareacceleration features provided by texture mapping hardware, treetraversal hardware or other graphics pipeline hardware.

Example Parallel Processing Architecture Including Ray Tracing

The TTU structure described above can be implemented in, or inassociation with, an example non-limiting parallel processing systemarchitecture such as that described below in relation to FIGS. 15-22.Such a parallel processing architecture can be used for example toimplement the GPU 130 of FIG. 1.

Example Parallel Processing Architecture

FIG. 15 illustrates an example non-limiting parallel processing unit(PPU) 1700. In an embodiment, the PPU 1700 is a multi-threaded processorthat is implemented on one or more integrated circuit devices. The PPU1700 is a latency hiding architecture designed to process many threadsin parallel. A thread (i.e., a thread of execution) is an instantiationof a set of instructions configured to be executed by the PPU 1700. Inan embodiment, the PPU 1700 is configured to implement a graphicsrendering pipeline for processing three-dimensional (3D) graphics datain order to generate two-dimensional (2D) image data for display on adisplay device such as a liquid crystal display (LCD) device, an organiclight emitting diode (OLED) device, a transparent light emitting diode(TOLED) device, a field emission display (FEDs), a field sequentialdisplay, a projection display, a head mounted display or any otherdesired display. In other embodiments, the PPU 1700 may be utilized forperforming general-purpose computations. While one exemplary parallelprocessor is provided herein for illustrative purposes, it should benoted that such processor is set forth for illustrative purposes only,and that any processor may be employed to supplement and/or substitutefor the same.

For example, one or more PPUs 1700 may be configured to acceleratethousands of High Performance Computing (HPC), data center, and machinelearning applications. The PPU 1700 may be configured to acceleratenumerous deep learning systems and applications including autonomousvehicle platforms, deep learning, high-accuracy speech, image, and textrecognition systems, intelligent video analytics, molecular simulations,drug discovery, disease diagnosis, weather forecasting, big dataanalytics, astronomy, molecular dynamics simulation, financial modeling,robotics, factory automation, real-time language translation, onlinesearch optimizations, and personalized user recommendations, and thelike.

The PPU 1700 may be included in a desktop computer, a laptop computer, atablet computer, servers, supercomputers, a smart-phone (e.g., awireless, hand-held device), personal digital assistant (PDA), a digitalcamera, a vehicle, a head mounted display, a hand-held electronicdevice, and the like. In an embodiment, the PPU 1700 is embodied on asingle semiconductor substrate. In another embodiment, the PPU 1700 isincluded in a system-on-a-chip (SoC) along with one or more otherdevices such as additional PPUs 1700, the memory 1704, a reducedinstruction set computer (RISC) CPU, a memory management unit (MMU), adigital-to-analog converter (DAC), and the like.

In an embodiment, the PPU 1700 may be included on a graphics card thatincludes one or more memory devices 1704. The graphics card may beconfigured to interface with a PCIe slot on a motherboard of a desktopcomputer. In yet another embodiment, the PPU 1700 may be an integratedgraphics processing unit (iGPU) or parallel processor included in thechipset of the motherboard.

As shown in FIG. 15, the PPU 1700 includes an Input/Output (I/O) unit1705, a front end unit 1715, a scheduler unit 1720, a work distributionunit 1725, a hub 1730, a crossbar (Xbar) 1770, one or more generalprocessing clusters (GPCs) 1750, and one or more partition units 1780.The PPU 1700 may be connected to a host processor or other PPUs 1700 viaone or more high-speed NVLink 1710 interconnect. The PPU 1700 may beconnected to a host processor or other peripheral devices via aninterconnect 1702. The PPU 1700 may also be connected to a local memorycomprising a number of memory devices 1704. In an embodiment, the localmemory may comprise a number of dynamic random access memory (DRAM)devices. The DRAM devices may be configured as a high-bandwidth memory(HBM) subsystem, with multiple DRAM dies stacked within each device.

The NVLink 1710 interconnect enables systems to scale and include one ormore PPUs 1700 combined with one or more CPUs, supports cache coherencebetween the PPUs 1700 and CPUs, and CPU mastering. Data and/or commandsmay be transmitted by the NVLink 1710 through the hub 1730 to/from otherunits of the PPU 1700 such as one or more copy engines, a video encoder,a video decoder, a power management unit, etc. (not explicitly shown).The NVLink 1710 is described in more detail in conjunction with FIG. 21.

The I/O unit 1705 is configured to transmit and receive communications(i.e., commands, data, etc.) from a host processor (not shown) over theinterconnect 1702. The I/O unit 1705 may communicate with the hostprocessor directly via the interconnect 1702 or through one or moreintermediate devices such as a memory bridge. In an embodiment, the I/Ounit 1705 may communicate with one or more other processors, such as oneor more of the PPUs 1700 via the interconnect 1702. In an embodiment,the I/O unit 1705 implements a Peripheral Component Interconnect Express(PCIe) interface for communications over a PCIe bus and the interconnect1702 is a PCIe bus. In alternative embodiments, the I/O unit 1705 mayimplement other types of well-known interfaces for communicating withexternal devices.

The I/O unit 1705 decodes packets received via the interconnect 1702. Inan embodiment, the packets represent commands configured to cause thePPU 1700 to perform various operations. The I/O unit 1705 transmits thedecoded commands to various other units of the PPU 1700 as the commandsmay specify. For example, some commands may be transmitted to the frontend unit 1715. Other commands may be transmitted to the hub 1730 orother units of the PPU 1700 such as one or more copy engines, a videoencoder, a video decoder, a power management unit, etc. (not explicitlyshown). In other words, the I/O unit 1705 is configured to routecommunications between and among the various logical units of the PPU1700.

In an embodiment, a program executed by the host processor encodes acommand stream in a buffer that provides workloads to the PPU 1700 forprocessing. A workload may comprise several instructions and data to beprocessed by those instructions. The buffer is a region in a memory thatis accessible (i.e., read/write) by both the host processor and the PPU1700. For example, the I/O unit 1705 may be configured to access thebuffer in a system memory connected to the interconnect 1702 via memoryrequests transmitted over the interconnect 1702. In an embodiment, thehost processor writes the command stream to the buffer and thentransmits a pointer to the start of the command stream to the PPU 1700.The front end unit 1715 receives pointers to one or more commandstreams. The front end unit 1715 manages the one or more streams,reading commands from the streams and forwarding commands to the variousunits of the PPU 1700.

The front end unit 1715 is coupled to a scheduler unit 1720 thatconfigures the various GPCs 1750 to process tasks defined by the one ormore streams. The scheduler unit 1720 is configured to track stateinformation related to the various tasks managed by the scheduler unit1720. The state may indicate which GPC 1750 a task is assigned to,whether the task is active or inactive, a priority level associated withthe task, and so forth. The scheduler unit 1720 manages the execution ofa plurality of tasks on the one or more GPCs 1750.

The scheduler unit 1720 is coupled to a work distribution unit 1725 thatis configured to dispatch tasks for execution on the GPCs 1750. The workdistribution unit 1725 may track a number of scheduled tasks receivedfrom the scheduler unit 1720. In an embodiment, the work distributionunit 1725 manages a pending task pool and an active task pool for eachof the GPCs 1750. The pending task pool may comprise a number of slots(e.g., 32 slots) that contain tasks assigned to be processed by aparticular GPC 1750. The active task pool may comprise a number of slots(e.g., 4 slots) for tasks that are actively being processed by the GPCs1750. As a GPC 1750 finishes the execution of a task, that task isevicted from the active task pool for the GPC 1750 and one of the othertasks from the pending task pool is selected and scheduled for executionon the GPC 1750. If an active task has been idle on the GPC 1750, suchas while waiting for a data dependency to be resolved, then the activetask may be evicted from the GPC 1750 and returned to the pending taskpool while another task in the pending task pool is selected andscheduled for execution on the GPC 1750.

The work distribution unit 1725 communicates with the one or more GPCs1750 via XBar 1770. The XBar 1770 is an interconnect network thatcouples many of the units of the PPU 1700 to other units of the PPU1700. For example, the XBar 1770 may be configured to couple the workdistribution unit 1725 to a particular GPC 1750. Although not shownexplicitly, one or more other units of the PPU 1700 may also beconnected to the XBar 1770 via the hub 1730.

The tasks are managed by the scheduler unit 1720 and dispatched to a GPC1750 by the work distribution unit 1725. The GPC 1750 is configured toprocess the task and generate results. The results may be consumed byother tasks within the GPC 1750, routed to a different GPC 1750 via theXBar 1770, or stored in the memory 1704. The results can be written tothe memory 1704 via the partition units 1780, which implement a memoryinterface for reading and writing data to/from the memory 1704. Theresults can be transmitted to another PPU 1704 or CPU via the NVLink1710. In an embodiment, the PPU 1700 includes a number U of partitionunits 1780 that is equal to the number of separate and distinct memorydevices 1704 coupled to the PPU 1700. A partition unit 1780 will bedescribed in more detail below in conjunction with FIG. 16.

In an embodiment, a host processor (e.g., processor 120 of FIG. 1)executes a driver kernel that implements an application programminginterface (API) that enables one or more applications executing on thehost processor to schedule operations for execution on the PPU 1700. Inan embodiment, multiple compute applications are simultaneously executedby the PPU 1700 and the PPU 1700 provides isolation, quality of service(QoS), and independent address spaces for the multiple computeapplications. An application may generate instructions (i.e., API calls)that cause the driver kernel to generate one or more tasks for executionby the PPU 1700. The driver kernel outputs tasks to one or more streamsbeing processed by the PPU 1700. Each task may comprise one or moregroups of related threads, referred to herein as a warp. In anembodiment, a warp comprises 32 related threads that may be executed inparallel. Cooperating threads may refer to a plurality of threadsincluding instructions to perform the task and that may exchange datathrough shared memory. Threads and cooperating threads are described inmore detail in conjunction with FIG. 19.

Example Memory Partition Unit

The MMU 1890 provides an interface between the GPC 1750 and thepartition unit 1780. The MMU 1890 may provide translation of virtualaddresses into physical addresses, memory protection, and arbitration ofmemory requests. In an embodiment, the MMU 1890 provides one or moretranslation lookaside buffers (TLBs) for performing translation ofvirtual addresses into physical addresses in the memory 1704.

FIG. 16 illustrates a memory partition unit 1780 of the PPU 1700 of FIG.15, in accordance with an embodiment. As shown in FIG. 16, the memorypartition unit 1780 includes a Raster Operations (ROP) unit 1850, alevel two (L2) cache 1860, and a memory interface 1870. The memoryinterface 1870 is coupled to the memory 1704. Memory interface 1870 mayimplement 32, 64, 128, 1024-bit data buses, or the like, for high-speeddata transfer. In an embodiment, the PPU 1700 incorporates U memoryinterfaces 1870, one memory interface 1870 per pair of partition units1780, where each pair of partition units 1780 is connected to acorresponding memory device 1704. For example, PPU 1700 may be connectedto up to Y memory devices 1704, such as high bandwidth memory stacks orgraphics double-data-rate, version 5, synchronous dynamic random accessmemory, or other types of persistent storage.

In an embodiment, the memory interface 1870 implements an HBM2 memoryinterface and Y equals half U. In an embodiment, the HBM2 memory stacksare located on the same physical package as the PPU 1700, providingsubstantial power and area savings compared with conventional GDDR5SDRAM systems. In an embodiment, each HBM2 stack includes four memorydies and Y equals 4, with HBM2 stack including two 128-bit channels perdie for a total of 8 channels and a data bus width of 1024 bits.

In an embodiment, the memory 1704 supports Single-Error CorrectingDouble-Error Detecting (SECDED) Error Correction Code (ECC) to protectdata. ECC provides higher reliability for compute applications that aresensitive to data corruption. Reliability is especially important inlarge-scale cluster computing environments where PPUs 1700 process verylarge datasets and/or run applications for extended periods.

In an embodiment, the PPU 1700 implements a multi-level memoryhierarchy. In an embodiment, the memory partition unit 1780 supports aunified memory to provide a single unified virtual address space for CPUand PPU 1700 memory, enabling data sharing between virtual memorysystems. In an embodiment the frequency of accesses by a PPU 1700 tomemory located on other processors is traced to ensure that memory pagesare moved to the physical memory of the PPU 1700 that is accessing thepages more frequently. In an embodiment, the NVLink 1710 supportsaddress translation services allowing the PPU 1700 to directly access aCPU's page tables and providing full access to CPU memory by the PPU1700.

In an embodiment, copy engines transfer data between multiple PPUs 1700or between PPUs 1700 and CPUs. The copy engines can generate page faultsfor addresses that are not mapped into the page tables. The memorypartition unit 1780 can then service the page faults, mapping theaddresses into the page table, after which the copy engine can performthe transfer. In a conventional system, memory is pinned (i.e.,non-pageable) for multiple copy engine operations between multipleprocessors, substantially reducing the available memory. With hardwarepage faulting, addresses can be passed to the copy engines withoutworrying if the memory pages are resident, and the copy process istransparent.

Data from the memory 1704 or other system memory may be fetched by thememory partition unit 1780 and stored in the L2 cache 1860, which islocated on-chip and is shared between the various GPCs 1750. As shown,each memory partition unit 1780 includes a portion of the L2 cache 1860associated with a corresponding memory device 1704. Lower level cachesmay then be implemented in various units within the GPCs 1750. Forexample, each of the SMs 1840 may implement a level one (L1) cache. TheL1 cache is private memory that is dedicated to a particular SM 1840.Data from the L2 cache 1860 may be fetched and stored in each of the L1caches for processing in the functional units of the SMs 1840. The L2cache 1860 is coupled to the memory interface 1870 and the XBar 1770.

The ROP unit 1850 performs graphics raster operations related to pixelcolor, such as color compression, pixel blending, and the like. The ROPunit 1850 also implements depth testing in conjunction with the rasterengine 1825, receiving a depth for a sample location associated with apixel fragment from the culling engine of the raster engine 1825. Thedepth is tested against a corresponding depth in a depth buffer for asample location associated with the fragment. If the fragment passes thedepth test for the sample location, then the ROP unit 1850 updates thedepth buffer and transmits a result of the depth test to the rasterengine 1825. It will be appreciated that the number of partition units1780 may be different than the number of GPCs 1750 and, therefore, eachROP unit 1850 may be coupled to each of the GPCs 1750. The ROP unit 1850tracks packets received from the different GPCs 1750 and determineswhich GPC 1750 that a result generated by the ROP unit 1850 is routed tothrough the Xbar 1770. Although the ROP unit 1850 is included within thememory partition unit 1780 in FIG. 16, in other embodiment, the ROP unit1850 may be outside of the memory partition unit 1780. For example, theROP unit 1850 may reside in the GPC 1750 or another unit.

Example General Processing Clusters

FIG. 17 illustrates a GPC 1750 of the PPU 1700 of FIG. 15, in accordancewith an embodiment. As shown in FIG. 17, each GPC 1750 includes a numberof hardware units for processing tasks. In an embodiment, each GPC 1750includes a pipeline manager 1810, a pre-raster operations unit (PROP)1815, a raster engine 1825, a work distribution crossbar (WDX) 1880, amemory management unit (MMU) 1890, and one or more Data ProcessingClusters (DPCs) 1820. It will be appreciated that the GPC 1750 of FIG.17 may include other hardware units in lieu of or in addition to theunits shown in FIG. 17.

In an embodiment, the operation of the GPC 1750 is controlled by thepipeline manager 1810. The pipeline manager 1810 manages theconfiguration of the one or more DPCs 1820 for processing tasksallocated to the GPC 1750. In an embodiment, the pipeline manager 1810may configure at least one of the one or more DPCs 1820 to implement atleast a portion of a graphics rendering pipeline.

Each DPC 1820 included in the GPC 1750 includes an M-Pipe Controller(MPC) 1830, a primitive engine 1835, one or more SMs 1840, one or moreTexture Units 1842, and one or more TTUs 700. The SM 1840 may bestructured similarly to SM 132 described above. The MPC 1830 controlsthe operation of the DPC 1820, routing packets received from thepipeline manager 1810 to the appropriate units in the DPC 1820. Forexample, packets associated with a vertex may be routed to the primitiveengine 1835, which is configured to fetch vertex attributes associatedwith the vertex from the memory 1704. In contrast, packets associatedwith a shader program may be transmitted to the SM 1840.

When configured for general purpose parallel computation, a simplerconfiguration can be used compared with graphics processing.Specifically, the fixed function graphics processing units shown in FIG.15, are bypassed, creating a much simpler programming model. In thegeneral purpose parallel computation configuration, the workdistribution unit 1725 assigns and distributes blocks of threadsdirectly to the DPCs 1820. The threads in a block execute the sameprogram, using a unique thread ID in the calculation to ensure eachthread generates unique results, using the SM 1840 to execute theprogram and perform calculations, shared memory/L1 cache 1970 tocommunicate between threads, and the LSU 1954 to read and write globalmemory through the shared memory/L1 cache 1970 and the memory partitionunit 1780. When configured for general purpose parallel computation, theSM 1840 can also write commands that the scheduler unit 1720 can use tolaunch new work on the DPCs 1820. The TTU 700 can be used to acceleratespatial queries in the context of general purpose computation.

Graphics Rendering Pipeline

A DPC 1820 may be configured to execute a vertex shader program on theprogrammable streaming multiprocessor (SM) 1840 which may acceleratecertain shading operations with TTU 700. The pipeline manager 1810 mayalso be configured to route packets received from the work distributionunit 1725 to the appropriate logical units within the GPC 1750. Forexample, some packets may be routed to fixed function hardware units inthe PROP 1815 and/or raster engine 1825 while other packets may berouted to the DPCs 1820 for processing by the primitive engine 1835 orthe SM 1840. In an embodiment, the pipeline manager 1810 may configureat least one of the one or more DPCs 1820 to implement a neural networkmodel and/or a computing pipeline.

The PROP unit 1815 is configured to route data generated by the rasterengine 1825 and the DPCs 1820 to a Raster Operations (ROP) unit,described in more detail in conjunction with FIG. 16. The PROP unit 1815may also be configured to perform optimizations for color blending,organize pixel data, perform address translations, and the like.

The raster engine 1825 includes a number of fixed function hardwareunits configured to perform various raster operations. In an embodiment,the raster engine 1825 includes a setup engine, a coarse raster engine,a culling engine, a clipping engine, a fine raster engine, and a tilecoalescing engine. The setup engine receives transformed vertices andgenerates plane equations associated with the geometric primitivedefined by the vertices. The plane equations are transmitted to thecoarse raster engine to generate coverage information (e.g., an x,ycoverage mask for a tile) for the primitive. The output of the coarseraster engine is transmitted to the culling engine where fragmentsassociated with the primitive that fail a z-test are culled, andnon-culled fragments are transmitted to a clipping engine wherefragments lying outside a viewing frustum are clipped. Those fragmentsthat survive clipping and culling may be passed to the fine rasterengine to generate attributes for the pixel fragments based on the planeequations generated by the setup engine. The output of the raster engine1825 comprises fragments to be processed, for example, by a fragmentshader implemented within a DPC 1820

In more detail, the PPU 1700 is configured to receive commands thatspecify shader programs for processing graphics data. Graphics data maybe defined as a set of primitives such as points, lines, triangles,quads, triangle strips, and the like. Typically, a primitive includesdata that specifies a number of vertices for the primitive (e.g., in amodel-space coordinate system) as well as attributes associated witheach vertex of the primitive. The PPU 1700 can be configured to processthe graphics primitives to generate a frame buffer (i.e., pixel data foreach of the pixels of the display) using for example TTU 700 as ahardware acceleration resource.

An application writes model data for a scene (i.e., a collection ofvertices and attributes) to a memory such as a system memory or memory1704. The model data defines each of the objects that may be visible ona display. The model data may also define one or more BVH's as describedabove. The application then makes an API call to the driver kernel thatrequests the model data to be rendered and displayed. The driver kernelreads the model data and writes commands to the one or more streams toperform operations to process the model data. The commands may referencedifferent shader programs to be implemented on the SMs 1840 of the PPU1700 including one or more of a vertex shader, hull shader, domainshader, geometry shader, a pixel shader, a ray generation shader, a rayintersection shader, a ray hit shader, and a ray miss shader (thesecorrespond to the shaders defined by the DirectX Raytracing (DXR) API,ignoring any distinction between “closest-hit” and “any-hit” shaders;seehttps://devblogs.nvidia.com/introduction-nvidia-rtx-directx-ray-tracing/).For example, one or more of the SMs 1840 may be configured to execute avertex shader program that processes a number of vertices defined by themodel data. In an embodiment, the different SMs 1840 may be configuredto execute different shader programs concurrently. For example, a firstsubset of SMs 1840 may be configured to execute a vertex shader programwhile a second subset of SMs 1840 may be configured to execute a pixelshader program. The first subset of SMs 1840 processes vertex data toproduce processed vertex data and writes the processed vertex data tothe L2 cache 1860 and/or the memory 1704 (see FIG. 16). After theprocessed vertex data is rasterized (i.e., transformed fromthree-dimensional data into two-dimensional data in screen space) toproduce fragment data, the second subset of SMs 1840 executes a pixelshader to produce processed fragment data, which is then blended withother processed fragment data and written to the frame buffer in memory1704. The vertex shader program and pixel shader program may executeconcurrently, processing different data from the same scene in apipelined fashion until all of the model data for the scene has beenrendered to the frame buffer. Then, the contents of the frame buffer aretransmitted to a display controller for display on a display device.

FIG. 18 is a conceptual diagram of a graphics processing pipeline 2000implemented by the PPU 1700 of FIG. 15. The graphics processing pipeline2000 is an abstract flow diagram of the processing steps implemented togenerate 2D computer-generated images from 3D geometry data. As iswell-known, pipeline architectures may perform long latency operationsmore efficiently by splitting up the operation into a plurality ofstages, where the output of each stage is coupled to the input of thenext successive stage. Thus, the graphics processing pipeline 2000receives input data 2001 that is transmitted from one stage to the nextstage of the graphics processing pipeline 2000 to generate output data2002. In an embodiment, the graphics processing pipeline 2000 mayrepresent a graphics processing pipeline defined by the OpenGL® API. Asan option, the graphics processing pipeline 2000 may be implemented inthe context of the functionality and architecture of the previousFigures and/or any subsequent Figure(s). As discussed above withreference to FIG. 14, the ray tracing may be used to improve the imagequality generated by rasterization of geometric primitives. In someembodiments, ray tracing operations and TTU structure disclosed in thisapplication may be applied to one or more states of the graphicsprocessing pipeline 2000 to improve the subjective image quality.

As shown in FIG. 18, the graphics processing pipeline 2000 comprises apipeline architecture that includes a number of stages. The stagesinclude, but are not limited to, a data assembly stage 2010, a vertexshading stage 2020, a primitive assembly stage 2030, a geometry shadingstage 2040, a viewport scale, cull, and clip (VSCC) stage 2050, arasterization stage 2060, a fragment shading stage 2070, and a rasteroperations stage 2080. In an embodiment, the input data 2001 comprisescommands that configure the processing units to implement the stages ofthe graphics processing pipeline 2000 and geometric primitives (e.g.,points, lines, triangles, quads, triangle strips or fans, etc.) to beprocessed by the stages. The output data 2002 may comprise pixel data(i.e., color data) that is copied into a frame buffer or other type ofsurface data structure in a memory.

The data assembly stage 2010 receives the input data 2001 that specifiesvertex data for high-order surfaces, primitives, or the like. The dataassembly stage 2010 collects the vertex data in a temporary storage orqueue, such as by receiving a command from the host processor thatincludes a pointer to a buffer in memory and reading the vertex datafrom the buffer. The vertex data is then transmitted to the vertexshading stage 2020 for processing.

The vertex shading stage 2020 processes vertex data by performing a setof operations (i.e., a vertex shader or a program) once for each of thevertices. Vertices may be, e.g., specified as a 4-coordinate vector(i.e., <x, y, z, w>) associated with one or more vertex attributes(e.g., color, texture coordinates, surface normal, etc.). The vertexshading stage 2020 may manipulate individual vertex attributes such asposition, color, texture coordinates, and the like. In other words, thevertex shading stage 2020 performs operations on the vertex coordinatesor other vertex attributes associated with a vertex. Such operationscommonly including lighting operations (i.e., modifying color attributesfor a vertex) and transformation operations (i.e., modifying thecoordinate space for a vertex). For example, vertices may be specifiedusing coordinates in an object-coordinate space, which are transformedby multiplying the coordinates by a matrix that translates thecoordinates from the object-coordinate space into a world space or anormalized-device-coordinate (NCD) space. The vertex shading stage 2020generates transformed vertex data that is transmitted to the primitiveassembly stage 2030.

The primitive assembly stage 2030 collects vertices output by the vertexshading stage 2020 and groups the vertices into geometric primitives forprocessing by the geometry shading stage 2040. For example, theprimitive assembly stage 2030 may be configured to group every threeconsecutive vertices as a geometric primitive (i.e., a triangle) fortransmission to the geometry shading stage 2040. In some embodiments,specific vertices may be reused for consecutive geometric primitives(e.g., two consecutive triangles in a triangle strip may share twovertices). The primitive assembly stage 2030 transmits geometricprimitives (i.e., a collection of associated vertices) to the geometryshading stage 2040.

The geometry shading stage 2040 processes geometric primitives byperforming a set of operations (i.e., a geometry shader or program) onthe geometric primitives. Tessellation operations may generate one ormore geometric primitives from each geometric primitive. In other words,the geometry shading stage 2040 may subdivide each geometric primitiveinto a finer mesh of two or more geometric primitives for processing bythe rest of the graphics processing pipeline 2000. The geometry shadingstage 2040 transmits geometric primitives to the viewport SCC stage2050.

In an embodiment, the graphics processing pipeline 2000 may operatewithin a streaming multiprocessor and the vertex shading stage 2020, theprimitive assembly stage 2030, the geometry shading stage 2040, thefragment shading stage 2070, a ray tracing shader, and/orhardware/software associated therewith, may sequentially performprocessing operations. Once the sequential processing operations arecomplete, in an embodiment, the viewport SCC stage 2050 may utilize thedata. In an embodiment, primitive data processed by one or more of thestages in the graphics processing pipeline 2000 may be written to acache (e.g. L1 cache, a vertex cache, etc.). In this case, in anembodiment, the viewport SCC stage 2050 may access the data in thecache. In an embodiment, the viewport SCC stage 2050 and therasterization stage 2060 are implemented as fixed function circuitry.

The viewport SCC stage 2050 performs viewport scaling, culling, andclipping of the geometric primitives. Each surface being rendered to isassociated with an abstract camera position. The camera positionrepresents a location of a viewer looking at the scene and defines aviewing frustum that encloses the objects of the scene. The viewingfrustum may include a viewing plane, a rear plane, and four clippingplanes. Any geometric primitive entirely outside of the viewing frustummay be culled (i.e., discarded) because the geometric primitive will notcontribute to the final rendered scene. Any geometric primitive that ispartially inside the viewing frustum and partially outside the viewingfrustum may be clipped (i.e., transformed into a new geometric primitivethat is enclosed within the viewing frustum. Furthermore, geometricprimitives may each be scaled based on a depth of the viewing frustum.All potentially visible geometric primitives are then transmitted to therasterization stage 2060.

The rasterization stage 2060 converts the 3D geometric primitives into2D fragments (e.g. capable of being utilized for display, etc.). Therasterization stage 2060 may be configured to utilize the vertices ofthe geometric primitives to setup a set of plane equations from whichvarious attributes can be interpolated. The rasterization stage 2060 mayalso compute a coverage mask for a plurality of pixels that indicateswhether one or more sample locations for the pixel intercept thegeometric primitive. In an embodiment, z-testing may also be performedto determine if the geometric primitive is occluded by other geometricprimitives that have already been rasterized. The rasterization stage2060 generates fragment data (i.e., interpolated vertex attributesassociated with a particular sample location for each covered pixel)that are transmitted to the fragment shading stage 2070.

The fragment shading stage 2070 processes fragment data by performing aset of operations (i.e., a fragment shader or a program) on each of thefragments. The fragment shading stage 2070 may generate pixel data(i.e., color values) for the fragment such as by performing lightingoperations or sampling texture maps using interpolated texturecoordinates for the fragment. The fragment shading stage 2070 generatespixel data that is transmitted to the raster operations stage 2080.

The raster operations stage 2080 may perform various operations on thepixel data such as performing alpha tests, stencil tests, and blendingthe pixel data with other pixel data corresponding to other fragmentsassociated with the pixel. When the raster operations stage 2080 hasfinished processing the pixel data (i.e., the output data 2002), thepixel data may be written to a render target such as a frame buffer, acolor buffer, or the like.

It will be appreciated that one or more additional stages may beincluded in the graphics processing pipeline 2000 in addition to or inlieu of one or more of the stages described above. Variousimplementations of the abstract graphics processing pipeline mayimplement different stages. Furthermore, one or more of the stagesdescribed above may be excluded from the graphics processing pipeline insome embodiments (such as the geometry shading stage 2040). Other typesof graphics processing pipelines are contemplated as being within thescope of the present disclosure. Furthermore, any of the stages of thegraphics processing pipeline 2000 may be implemented by one or morededicated hardware units within a graphics processor such as PPU 200.Other stages of the graphics processing pipeline 2000 may be implementedby programmable hardware units such as the SM 1840 of the PPU 1700.

The graphics processing pipeline 2000 may be implemented via anapplication executed by a host processor, such as a CPU 120. In anembodiment, a device driver may implement an application programminginterface (API) that defines various functions that can be utilized byan application in order to generate graphical data for display. Thedevice driver is a software program that includes a plurality ofinstructions that control the operation of the PPU 1700. The APIprovides an abstraction for a programmer that lets a programmer utilizespecialized graphics hardware, such as the PPU 1700, to generate thegraphical data without requiring the programmer to utilize the specificinstruction set for the PPU 1700. The application may include an APIcall that is routed to the device driver for the PPU 1700. The devicedriver interprets the API call and performs various operations torespond to the API call. In some instances, the device driver mayperform operations by executing instructions on the CPU. In otherinstances, the device driver may perform operations, at least in part,by launching operations on the PPU 1700 utilizing an input/outputinterface between the CPU and the PPU 1700. In an embodiment, the devicedriver is configured to implement the graphics processing pipeline 2000utilizing the hardware of the PPU 1700.

Various programs may be executed within the PPU 1700 in order toimplement the various stages of the graphics processing pipeline 2000.For example, the device driver may launch a kernel on the PPU 1700 toperform the vertex shading stage 2020 on one SM 1840 (or multiple SMs1840). The device driver (or the initial kernel executed by the PPU1800) may also launch other kernels on the PPU 1800 to perform otherstages of the graphics processing pipeline 2000, such as the geometryshading stage 2040 and the fragment shading stage 2070. In addition,some of the stages of the graphics processing pipeline 2000 may beimplemented on fixed unit hardware such as a rasterizer or a dataassembler implemented within the PPU 1800. It will be appreciated thatresults from one kernel may be processed by one or more interveningfixed function hardware units before being processed by a subsequentkernel on an SM 1840.

Example Streaming Multiprocessor

The SM 1840 comprises a programmable streaming processor that isconfigured to process tasks represented by a number of threads. Each SM1840 is multi-threaded and configured to execute a plurality of threads(e.g., 32 threads comprising a warp) from a particular group of threadsconcurrently. In an embodiment, the SM 1840 implements a SIMD(Single-Instruction, Multiple-Data) architecture where each thread in agroup of threads (i.e., a warp) is configured to process a different setof data based on the same set of instructions. All threads in the groupof threads execute the same instructions. In another embodiment, the SM1840 implements a SIMT (Single-Instruction, Multiple Thread)architecture where each thread in a group of threads is configured toprocess a different set of data based on the same set of instructions,but where individual threads in the group of threads are allowed todiverge during execution. In an embodiment, a program counter, callstack, and execution state is maintained for each warp, enablingconcurrency between warps and serial execution within warps when threadswithin the warp diverge. In another embodiment, a program counter, callstack, and execution state is maintained for each individual thread,enabling equal concurrency between all threads, within and betweenwarps. When execution state is maintained for each individual thread,threads executing the same instructions may be converged and executed inparallel for maximum efficiency.

FIG. 19 illustrates the streaming multi-processor 1840 of FIG. 17, inaccordance with an embodiment. As shown in FIG. 19, the SM 1840 includesan instruction cache 1905, one or more scheduler units 1910, a registerfile 1920, one or more processing cores 1950, one or more specialfunction units (SFUs) 1952, one or more load/store units (LSUs) 1954, aninterconnect network 1980, a shared memory/L1 cache 1970.

As described above, the work distribution unit 1725 dispatches tasks forexecution on the GPCs 1750 of the PPU 1700. The tasks are allocated to aparticular DPC 1820 within a GPC 1750 and, if the task is associatedwith a shader program, the task may be allocated to an SM 1840. Thescheduler unit 1910 receives the tasks from the work distribution unit1725 and manages instruction scheduling for one or more thread blocksassigned to the SM 1840. The scheduler unit 1910 schedules thread blocksfor execution as warps of parallel threads, where each thread block isallocated at least one warp. In an embodiment, each warp executes 32threads. The scheduler unit 1910 may manage a plurality of differentthread blocks, allocating the warps to the different thread blocks andthen dispatching instructions from the plurality of differentcooperative groups to the various functional units (i.e., cores 1950,SFUs 1952, and LSUs 1954) during each clock cycle.

Cooperative Groups is a programming model for organizing groups ofcommunicating threads that allows developers to express the granularityat which threads are communicating, enabling the expression of richer,more efficient parallel decompositions. Cooperative launch APIs supportsynchronization amongst thread blocks for the execution of parallelalgorithms. Conventional programming models provide a single, simpleconstruct for synchronizing cooperating threads: a barrier across allthreads of a thread block (i.e., the syncthreads( ) function). However,programmers would often like to define groups of threads at smaller thanthread block granularities and synchronize within the defined groups toenable greater performance, design flexibility, and software reuse inthe form of collective group-wide function interfaces.

Cooperative Groups enables programmers to define groups of threadsexplicitly at sub-block (i.e., as small as a single thread) andmulti-block granularities, and to perform collective operations such assynchronization on the threads in a cooperative group. The programmingmodel supports clean composition across software boundaries, so thatlibraries and utility functions can synchronize safely within theirlocal context without having to make assumptions about convergence.Cooperative Groups primitives enable new patterns of cooperativeparallelism, including producer-consumer parallelism, opportunisticparallelism, and global synchronization across an entire grid of threadblocks.

A dispatch unit 1915 is configured to transmit instructions to one ormore of the functional units. In the embodiment, the scheduler unit 1910includes two dispatch units 1915 that enable two different instructionsfrom the same warp to be dispatched during each clock cycle. Inalternative embodiments, each scheduler unit 1910 may include a singledispatch unit 1915 or additional dispatch units 1915.

Each SM 1840 includes a register file 1920 that provides a set ofregisters for the functional units of the SM 1840. In an embodiment, theregister file 1920 is divided between each of the functional units suchthat each functional unit is allocated a dedicated portion of theregister file 1920. In another embodiment, the register file 1920 isdivided between the different warps being executed by the SM 1840. Theregister file 1920 provides temporary storage for operands connected tothe data paths of the functional units. FIG. 20 illustrates an exampleconfiguration of the registers files in the SM 1840.

Each SM 1840 comprises L processing cores 1950. In an embodiment, the SM1840 includes a large number (e.g., 128, etc.) of distinct processingcores 1950. Each core 1950 may include a fully-pipelined,single-precision, double-precision, and/or mixed precision processingunit that includes a floating point arithmetic logic unit and an integerarithmetic logic unit. In an embodiment, the floating point arithmeticlogic units implement the IEEE 754-2008 standard for floating pointarithmetic. In an embodiment, the cores 1950 include 64 single-precision(32-bit) floating point cores, 64 integer cores, 32 double-precision(64-bit) floating point cores, and 8 tensor cores.

Tensor cores are configured to perform matrix operations, and, in anembodiment, one or more tensor cores are included in the cores 1950. Inparticular, the tensor cores are configured to perform deep learningmatrix arithmetic, such as convolution operations for neural networktraining and inferencing. In an embodiment, each tensor core operates ona 4×4 matrix and performs a matrix multiply and accumulate operationD=A><B+C, where A, B, C, and D are 4×4 matrices.

In an embodiment, the matrix multiply inputs A and B are 16-bit floatingpoint matrices, while the accumulation matrices C and D may be 16-bitfloating point or 32-bit floating point matrices. Tensor Cores operateon 16-bit floating point input data with 32-bit floating pointaccumulation. The 16-bit floating point multiply requires 64 operationsand results in a full precision product that is then accumulated using32-bit floating point addition with the other intermediate products fora 4×4×4 matrix multiply. In practice, Tensor Cores are used to performmuch larger two-dimensional or higher dimensional matrix operations,built up from these smaller elements. An API, such as CUDA 9 C++ API,exposes specialized matrix load, matrix multiply and accumulate, andmatrix store operations to efficiently use Tensor Cores from a CUDA-C++program. At the CUDA level, the warp-level interface assumes 16×16 sizematrices spanning all 32 threads of the warp.

Each SM 1840 also comprises M SFUs 1952 that perform special functions(e.g., attribute evaluation, reciprocal square root, and the like). Inan embodiment, the SFUs 1952 may include a tree traversal unitconfigured to traverse a hierarchical tree data structure. In anembodiment, the SFUs 1952 may include texture unit configured to performtexture map filtering operations. In an embodiment, the texture unitsare configured to load texture maps (e.g., a 2D array of texels) fromthe memory 1704 and sample the texture maps to produce sampled texturevalues for use in shader programs executed by the SM 1840. In anembodiment, the texture maps are stored in the shared memory/L1 cache1970. The texture units implement texture operations such as filteringoperations using mip-maps (i.e., texture maps of varying levels ofdetail). In an embodiment, each SM 1740 includes two texture units.

Each SM 1840 also comprises N LSUs 1954 that implement load and storeoperations between the shared memory/L1 cache 1970 and the register file1920. Each SM 1840 includes an interconnect network 1980 that connectseach of the functional units to the register file 1920 and the LSU 1954to the register file 1920, shared memory/L1 cache 1970. In anembodiment, the interconnect network 1980 is a crossbar that can beconfigured to connect any of the functional units to any of theregisters in the register file 1920 and connect the LSUs 1954 to theregister file and memory locations in shared memory/L1 cache 1970.

The shared memory/L1 cache 1970 is an array of on-chip memory thatallows for data storage and communication between the SM 1840 and theprimitive engine 1835 and between threads in the SM 1840. In anembodiment, the shared memory/L1 cache 1970 comprises 128 KB of storagecapacity and is in the path from the SM 1840 to the partition unit 1780.The shared memory/L1 cache 1970 can be used to cache reads and writes.One or more of the shared memory/L1 cache 1970, L2 cache 1860, andmemory 1704 are backing stores.

Combining data cache and shared memory functionality into a singlememory block provides the best overall performance for both types ofmemory accesses. The capacity is usable as a cache by programs that donot use shared memory. For example, if shared memory is configured touse half of the capacity, texture and load/store operations can use theremaining capacity. Integration within the shared memory/L1 cache 1970enables the shared memory/L1 cache 1970 to function as a high-throughputconduit for streaming data while simultaneously providing high-bandwidthand low-latency access to frequently reused data.

FIG. 20 illustrates one example architecture for the SM 1840. Asillustrated in FIG. 17, the SM 1840 may be coupled to one or moreTexture Unit 1842 and/or one or more TTUs 700. As a compromise betweenperformance and area, one example non-limiting embodiment may include asingle Texture Unit 1842 and/or a single TTU 700 per groups of SMs 1840(e.g., See FIG. 17). The TTU 700 may communicate with the SMs 1840 via aTTU input/output block in memory input-output and with a L1 cache via adedicated read interface. In one example embodiment, the TTU 700 onlyreads from the main memory and does not write to the main memory.

Example More Detailed TTU Architecture

As discussed above, the TTU 700 may be a coprocessor to the SM 1840.Like a texture processor, it is exposed via a set of SM instructions,accesses memory as a read-only client of the L1 cache, and returnsresults into the SM register file. Unlike some texture processors, theamount of data that may need to be passed into and out of the TTU 700for a typical query makes it difficult in some embodiments to specifyall the source and destination registers in a single instruction (andbecause most of this data is unique per-thread, there is no TTU analogueof texture headers and samplers). As a consequence, the TTU 700 in someembodiments is programmed via a multi-instruction sequence. Thissequence can be conceptualized as a single “macro-instruction” in someimplementations.

Also like a Texture Units 1842, the TTU 700 in some implementations mayrely on certain read-only data structures in memory that areprepopulated by software. These include:

-   -   One or more BVHs, where each BVH is for example a tree of        axis-aligned bounding boxes, stored in a compressed format that        greatly reduces memory traffic compared to an uncompressed        representation. Each node in the BVH is stored as a complet        structure, with size and alignment in some implementations        matched to that of an L1 cache line. Child complets of a given        parent are preferably stored contiguously in memory and child        pointers are stored in compressed form.    -   Zero or more instance nodes, which provide a way to connect a        leaf of one BVH to the root of another. An instance node may be        a data structure that is also aligned. This structure may        contain a pointer to the sub-BVH, flags that affect back-face        culling behavior in the sub-BVH, and a matrix that corresponds        to the first three rows of an arbitrary transformation matrix        (in homogeneous coordinates) from the coordinate system of the        top-level BVH (commonly “world space”) to that of the sub-BVH        (commonly “object space”). The final row of the matrix in some        embodiments is in some implementations implicitly (0, 0, 0, 1).    -   Zero or more triangle or other primitive buffers, containing for        example triangles stored either as a triplet of coordinates per        vertex or in a lossless compressed format understood by the TTU        700. In addition, an alpha bit may be provided per triangle or        other primitive, indicating triangles that require special        handling by software to determine whether the triangle is        actually intersected by a given ray. Triangle buffers can be        organized into blocks. There may also be a per-triangle        force-no-cull function bit. When set, that bit indicates that        both sides of the triangle should be treated as front-facing or        back-facing with respect to culling, i.e., the triangle should        not be culled because the ray intersects the “back” instead of        the “front”. The simplest use case for this is a single triangle        used to represent a leaf, where we can still see the leaf if the        ray hits it on the back surface.

The TTU 700 in some embodiments is stateless, meaning that noarchitectural state is maintained in the TTU between queries. At thesame time, it is often useful for software running on the SM 1840 torequest continuation of a previous query, which implies that relevantstate should be written to registers by the TTU 700 and then passed backto the TTU in registers (often in-place) to continue. This state maytake the form of a traversal stack that tracks progress in the traversalof the BVH.

A small number of stack initializers may also be provided for beginninga new query of a given type, for example:

-   -   Traversal starting from a complet    -   Intersection of a ray with a range of triangles    -   Intersection of a ray with a range of triangles, followed by        traversal starting from a complet    -   Vertex fetch from a triangle buffer for a given triangle    -   Optional support for instance transforms in front of the        “traversal starting from a complet” and “intersection of a ray        with a range of triangles”.

Vertex fetch is a simple query that may be specified with request datathat consists of a stack initializer and nothing else. Other query typesmay require the specification of a ray or beam, along with the stack orstack initializer and various ray flags describing details of the query.A ray is given by its three-coordinate origin, three-coordinatedirection, and minimum and maximum values for the t-parameter along theray. A beam is additionally given by a second origin and direction.

Various ray flags can be used to control various aspects of traversalbehavior, back-face culling, and handling of the various child nodetypes, subject to a pass/fail status of an optional rayOp test. RayOpsadd considerable flexibility to the capabilities of the TTU. In someexample embodiments, the RayOps portion introduces two Ray Flag versionscan be dynamically selected based on a specified operation on dataconveyed with the ray and data stored in the complet. To explore suchflags, it's first helpful to understand the different types of childnodes allowed within a BVH, as well as the various hit types that theTTU 700 can return to the SM. Example node types are:

-   -   A child complet (i.e., an internal node)        By default, the TTU 700 continues traversal by descending into        child complets.    -   A triangle range, corresponding to a contiguous set of triangles        within a triangle buffer    -   (1) By default, triangle ranges encountered by a ray are handled        natively by the TTU 700 by testing the triangles for        intersection and shortening the ray accordingly. If traversal        completes and a triangle was hit, default behavior is for the        triangle ID to be returned to SM 1840, along with the t-value        and barycentric coordinates of the intersection. This is the        “Triangle” hit type.    -   (2) By default, intersected triangles with the alpha bit set are        returned to SM 1840 even if traversal has not completed. The        returned traversal stack contains the state required to continue        traversal if software determines that the triangle was in fact        transparent.    -   (3) Triangle intersection in some embodiments is not supported        for beams, so encountered triangle ranges are by default        returned to SM 1840 as a “TriRange” hit type, which includes a        pointer to the first triangle block overlapping the range,        parameters specifying the range, and the t-value of the        intersection with the leaf bounding box.    -   An item range, consisting of an index (derived from a        user-provided “item range base” stored in the complet) and a        count of items.

By default, item ranges are returned to SM 1840 as an “ItemRange” hittype, consisting of for example an index, a count, and the t-value ofthe intersection with the leaf bounding box.

-   -   An instance node.

The TTU 700 in some embodiments can handle one level of instancingnatively by transforming the ray into the coordinate system of theinstance BVH. Additional levels of instancing (or every other level ofinstancing, depending on strategy) may be handled in software. The“InstanceNode” hit type is provided for this purpose, consisting of apointer to the instance node and the tvalue of the intersection with theleaf bounding box. In other implementations, the hardware can handletwo, three or more levels of instancing.

In addition to the node-specific hit types, a generic “NodeRef” hit typeis provided that consists of a pointer to the parent complet itself, aswell as an ID indicating which child was intersected and the t-value ofthe intersection with the bounding box of that child.

An “Error” hit type may be provided for cases where the query or BVH wasimproperly formed or if traversal encountered issues during traversal.

A “None” hit type may be provided for the case where the ray or beammisses all geometry in the scene.

How the TTU handles each of the four possible node types is determinedby a set of node-specific mode flags set as part of the query for agiven ray. The “default” behavior mentioned above corresponds to thecase where the mode flags are set to all zeroes.

Alternative values for the flags allow for culling all nodes of a giventype, returning nodes of a given type to SM as a NodeRef hit type, orreturning triangle ranges or instance nodes to SM using theircorresponding hit types, rather than processing them natively within theTTU 700.

Additional mode flags may be provided for control handling of alphatriangles.

Exemplary Computing System

Systems with multiple GPUs and CPUs are used in a variety of industriesas developers expose and leverage more parallelism in applications suchas artificial intelligence computing. High-performance GPU-acceleratedsystems with tens to many thousands of compute nodes are deployed indata centers, research facilities, and supercomputers to solve everlarger problems. As the number of processing devices within thehigh-performance systems increases, the communication and data transfermechanisms need to scale to support the increased data transmissionbetween the processing devices.

FIG. 21 is a conceptual diagram of a processing system 1900 implementedusing the PPU 1700 of FIG. 15, in accordance with an embodiment. Theexemplary system 1900 may be configured to implement one or more methodsdisclosed in this application. The processing system 1900 includes a CPU1930, switch 1912, and multiple PPUs 1700 each and respective memories1704. The NVLink 1710 provides high-speed communication links betweeneach of the PPUs 1700. Although a particular number of NVLink 1710 andinterconnect 1702 connections are illustrated in FIG. 21, the number ofconnections to each PPU 1700 and the CPU 1930 may vary. The switch 1912interfaces between the interconnect 1702 and the CPU 1930. The PPUs1700, memories 1704, and NVLinks 1710 may be situated on a singlesemiconductor platform to form a parallel processing module 1925. In anembodiment, the switch 1912 supports two or more protocols to interfacebetween various different connections and/or links.

In another embodiment (not shown), the NVLink 1710 provides one or morehigh-speed communication links between each of the PPUs 1700 and the CPU1930 and the switch 1912 interfaces between the interconnect 1702 andeach of the PPUs 1700. The PPUs 1700, memories 1704, and interconnect1702 may be situated on a single semiconductor platform to form aparallel processing module 1925. In yet another embodiment (not shown),the interconnect 1702 provides one or more communication links betweeneach of the PPUs 1700 and the CPU 1930 and the switch 1912 interfacesbetween each of the PPUs 1700 using the NVLink 1710 to provide one ormore high-speed communication links between the PPUs 1700. In anotherembodiment (not shown), the NVLink 1710 provides one or more high-speedcommunication links between the PPUs 1700 and the CPU 1930 through theswitch 1912. In yet another embodiment (not shown), the interconnect1702 provides one or more communication links between each of the PPUs1700 directly. One or more of the NVLink 1710 high-speed communicationlinks may be implemented as a physical NVLink interconnect or either anon-chip or on-die interconnect using the same protocol as the NVLink1710.

In the context of the present description, a single semiconductorplatform may refer to a sole unitary semiconductor-based integratedcircuit fabricated on a die or chip. It should be noted that the termsingle semiconductor platform may also refer to multi-chip modules withincreased connectivity which simulate on-chip operation and makesubstantial improvements over utilizing a conventional busimplementation. Of course, the various circuits or devices may also besituated separately or in various combinations of semiconductorplatforms per the desires of the user. Alternately, the parallelprocessing module 1925 may be implemented as a circuit board substrateand each of the PPUs 1700 and/or memories 1704 may be packaged devices.In an embodiment, the CPU 1930, switch 1912, and the parallel processingmodule 1925 are situated on a single semiconductor platform.

In an embodiment, the signaling rate of each NVLink 1710 is 20 to 25Gigabits/second and each PPU 1700 includes six NVLink 1710 interfaces(as shown in FIG. 21, five NVLink 1710 interfaces are included for eachPPU 1700). Each NVLink 1710 provides a data transfer rate of 25Gigabytes/second in each direction, with six links providing 1700Gigabytes/second. The NVLinks 1710 can be used exclusively forPPU-to-PPU communication as shown in FIG. 21, or some combination ofPPU-to-PPU and PPU-to-CPU, when the CPU 1930 also includes one or moreNVLink 1710 interfaces.

In an embodiment, the NVLink 1710 allows direct load/store/atomic accessfrom the CPU 1930 to each PPU's 1700 memory 1704. In an embodiment, theNVLink 1710 supports coherency operations, allowing data read from thememories 1704 to be stored in the cache hierarchy of the CPU 1930,reducing cache access latency for the CPU 1930. In an embodiment, theNVLink 1710 includes support for Address Translation Services (ATS),allowing the PPU 1700 to directly access page tables within the CPU1930. One or more of the NVLinks 1710 may also be configured to operatein a low-power mode.

FIG. 22 illustrates an exemplary system 1965 in which the variousarchitecture and/or functionality of the various previous embodimentsmay be implemented. The exemplary system 1965 may be configured toimplement one or more methods disclosed in this application.

As shown, a system 1965 is provided including at least one centralprocessing unit 1930 that is connected to a communication bus 1975. Thecommunication bus 1975 may be implemented using any suitable protocol,such as PCI (Peripheral Component Interconnect), PCI-Express, AGP(Accelerated Graphics Port), HyperTransport, or any other bus orpoint-to-point communication protocol(s). The system 1965 also includesa main memory 1940. Control logic (software) and data are stored in themain memory 1940 which may take the form of random access memory (RAM).

The system 1965 also includes input devices 1960, the parallelprocessing system 1925, and display devices 1945, i.e. a conventionalCRT (cathode ray tube), LCD (liquid crystal display), LED (lightemitting diode), plasma display or the like. User input may be receivedfrom the input devices 1960, e.g., keyboard, mouse, touchpad,microphone, and the like. Each of the foregoing modules and/or devicesmay even be situated on a single semiconductor platform to form thesystem 1965. Alternately, the various modules may also be situatedseparately or in various combinations of semiconductor platforms per thedesires of the user.

Further, the system 1965 may be coupled to a network (e.g., atelecommunications network, local area network (LAN), wireless network,wide area network (WAN) such as the Internet, peer-to-peer network,cable network, or the like) through a network interface 1935 forcommunication purposes.

The system 1965 may also include a secondary storage (not shown). Thesecondary storage includes, for example, a hard disk drive and/or aremovable storage drive, representing a floppy disk drive, a magnetictape drive, a compact disk drive, digital versatile disk (DVD) drive,recording device, universal serial bus (USB) flash memory. The removablestorage drive reads from and/or writes to a removable storage unit in awell-known manner.

Computer programs, or computer control logic algorithms, may be storedin the main memory 1940 and/or the secondary storage. Such computerprograms, when executed, enable the system 1965 to perform variousfunctions. The memory 1940, the storage, and/or any other storage arepossible examples of computer-readable media.

The architecture and/or functionality of the various previous figuresmay be implemented in the context of a general computer system, acircuit board system, a game console system dedicated for entertainmentpurposes, an application-specific system, and/or any other desiredsystem. For example, the system 1965 may take the form of a desktopcomputer, a laptop computer, a tablet computer, servers, supercomputers,a smart-phone (e.g., a wireless, hand-held device), personal digitalassistant (PDA), a digital camera, a vehicle, a head mounted display, ahand-held electronic device, a mobile phone device, a television,workstation, game consoles, embedded system, and/or any other type oflogic.

Machine Learning

Deep neural networks (DNNs) developed on processors, such as the PPU1700 have been used for diverse use cases, from self-driving cars tofaster drug development, from automatic image captioning in online imagedatabases to smart real-time language translation in video chatapplications. Deep learning is a technique that models the neurallearning process of the human brain, continually learning, continuallygetting smarter, and delivering more accurate results more quickly overtime. A child is initially taught by an adult to correctly identify andclassify various shapes, eventually being able to identify shapeswithout any coaching. Similarly, a deep learning or neural learningsystem needs to be trained in object recognition and classification forit get smarter and more efficient at identifying basic objects, occludedobjects, etc., while also assigning context to objects.

At the simplest level, neurons in the human brain look at various inputsthat are received, importance levels are assigned to each of theseinputs, and output is passed on to other neurons to act upon. Anartificial neuron or perceptron is the most basic model of a neuralnetwork. In one example, a perceptron may receive one or more inputsthat represent various features of an object that the perceptron isbeing trained to recognize and classify, and each of these features isassigned a certain weight based on the importance of that feature indefining the shape of an object.

A deep neural network (DNN) model includes multiple layers of manyconnected perceptrons (e.g., nodes) that can be trained with enormousamounts of input data to quickly solve complex problems with highaccuracy. In one example, a first layer of the DLL model breaks down aninput image of an automobile into various sections and looks for basicpatterns such as lines and angles. The second layer assembles the linesto look for higher level patterns such as wheels, windshields, andmirrors. The next layer identifies the type of vehicle, and the finalfew layers generate a label for the input image, identifying the modelof a specific automobile brand.

Once the DNN is trained, the DNN can be deployed and used to identifyand classify objects or patterns in a process known as inference.Examples of inference (the process through which a DNN extracts usefulinformation from a given input) include identifying handwritten numberson checks deposited into ATM machines, identifying images of friends inphotos, delivering movie recommendations to over fifty million users,identifying and classifying different types of automobiles, pedestrians,and road hazards in driverless cars, or translating human speech inreal-time.

During training, data flows through the DNN in a forward propagationphase until a prediction is produced that indicates a labelcorresponding to the input. If the neural network does not correctlylabel the input, then errors between the correct label and the predictedlabel are analyzed, and the weights are adjusted for each feature duringa backward propagation phase until the DNN correctly labels the inputand other inputs in a training dataset. Training complex neural networksrequires massive amounts of parallel computing performance, includingfloating-point multiplications and additions that are supported by thePPU 1700. Inferencing is less compute-intensive than training, being alatency-sensitive process where a trained neural network is applied tonew inputs it has not seen before to classify images, translate speech,and generally infer new information.

Neural networks rely heavily on matrix math operations, and complexmulti-layered networks require tremendous amounts of floating-pointperformance and bandwidth for both efficiency and speed. With thousandsof processing cores, optimized for matrix math operations, anddelivering tens to hundreds of TFLOPS of performance, the PPU 1700 is acomputing platform capable of delivering performance required for deepneural network-based artificial intelligence and machine learningapplications.

All patents & publications cited above are incorporated by reference asif expressly set forth.

While the invention has been described in connection with what ispresently considered to be the most practical and preferred embodiments,it is to be understood that the invention is not to be limited to thedisclosed embodiments, but on the contrary, is intended to cover variousmodifications and equivalent arrangements included within the spirit andscope of the appended claims.

1. A streaming cache for use by a ray tracer, the streaming cachecomprising: at least one cache line; a hit/miss detection circuit thatdetermines whether memory access requests from ray operations are hitsor misses, the circuit initiating data retrieval from memory into saidat least one cache line in response to detected misses; and a datapaththat time-coherently serves data to groups of ray operations thatrequest the same cache line.
 2. A method for using a cache to scheduleexecution of requests, the method comprising: determining whether afirst request uses data that is used by a second request; if it isdetermined that the first request uses the same data used by the secondrequest, grouping the first and second requests; and upon the databecoming available in the cache, delivering the data in response to bothfirst and second requests at about the same time thereby scheduling thegrouped first and second requests for execution.
 3. The method of claim2, wherein the first and second requests are requests to determineintersections between rays and volumetric bounding boxes.
 4. A streamingcache for use by a ray tracer, the streaming cache comprising: ahit/miss detection circuit that determines whether memory accessrequests to the cache from ray operations are hits or misses, thecircuit initiating data retrieval from memory in response to detectedmisses; and a datapath that time-coherently serves data retrieved fromthe memory in response to detected misses to groups of ray operationsthat request the same retrieved data.
 5. The streaming cache of claim 4wherein the datapath by time-coherently serving data retrieved from thememory in response to detected misses to groups of ray operations thatrequest the same retrieved data thereby schedules successive rayoperations traversing the same part of a bounding volume hierarchy to beperformed time-coherently.
 6. The streaming cache of claim 4 furtherincluding a pending address table and a pending request table, thepending address table containing addresses of data being retrieved frommemory by the circuit in response to detected misses, the pendingrequest table having multiple entries that reference the same pendingaddress table entry, said multiple pending request table entriescorresponding to a said one of the groups of ray operations.
 7. Thestreaming cache of claim 4 wherein the ray operations comprise traversalof a bounding volume hierarchy.
 8. The streaming cache of claim 4wherein the memory includes a cache memory that backs the streamingcache.
 9. The streaming cache of claim 4 wherein the circuit includes atag structure.
 10. The streaming cache of claim 9 wherein the tagstructure is linked to a pending address table.
 11. The streaming cacheof claim 9 wherein the tag structure is not linked to a pending addresstable.
 12. The streaming cache of claim 4 wherein the circuit isstructured to drop responses from memory if there is no room to storethe responses in cache lines.
 13. The streaming cache of claim 12wherein the circuit is further structured to accept no new requests iftoo many responses from memory are dropped.
 14. A streaming cache foruse by a ray tracer, the streaming cache comprising: at least one cacheline; a hit/miss detection circuit that determines whether memory accessrequests from ray operations are hits or misses, the circuit initiatingdata retrieval from memory into said at least one cache line in responseto detected misses; and a datapath that serves, at the same time orabout the same time, data to groups of independent ray operations thatrequest the same cache line to thereby schedule the independent rayoperations.
 15. A cache streaming method comprising: determining whethermemory access requests from ray operations are hits or misses in a cachememory; requesting data from main memory in response to detected misses;and time-coherently streaming, via a datapath, data the main memoryprovides based on requests responsive to detected misses to groups ofray operations that need the same retrieved data.
 16. The cachestreaming method of claim 15 wherein the time-coherently streaming datavia the datapath thereby schedules time-coherent performance ofsuccessive ray operations traversing the same part of a bounding volumehierarchy.
 17. The cache streaming method of claim 15 further includingstoring, in a pending address table, addresses of data requested frommain memory in response to detected cache memory misses.
 18. The cachestreaming method of claim 17 further including storing, in a pendingrequest table, multiple entries that reference the same pending addresstable entry, said multiple pending request table entries correspondingto a said one of the groups of ray operations.
 19. The cache streamingmethod of claim 15 further including traversing a bounding volumehierarchy with ray operations.
 20. The cache streaming method of claim15 further including backing the cache memory with the main memory. 21.The cache streaming method of claim 15 further including maintaining atag structure for the cache memory.
 22. The cache streaming method ofclaim 21 further including linking the tag structure to a pendingaddress table.
 23. The cache streaming method of claim 21 furtherincluding not linking the tag structure to a pending address table. 24.The cache streaming method of claim 15 further including dropping aresponse from main memory if there is no room in cache memory to storethe response.
 25. The cache streaming of claim 24 further includingaccepting no new requests from ray processing if too many responses frommain memory are dropped.
 26. A method comprising: determining thatplural independent rays request common data; and providing data to theplural independent rays determined to request the common data to therebysynchronize the independent rays to process said common data.
 27. Themethod of claim 26 wherein the determining and providing are performedby a streaming cache in response to memory read requests.